Dissertations / Theses: 'Neuro-fuzzy logic'

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Nukala, Ramesh Babu. "Neuro-fuzzy controllers for unstable systems." Thesis, Lancaster University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364362.

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Dalecký, Štěpán. "Neuro-fuzzy systémy." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236066.

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The thesis deals with artificial neural networks theory. Subsequently, fuzzy sets are being described and fuzzy logic is explained. The hybrid neuro-fuzzy system stemming from ANFIS system is designed on the basis of artificial neural networks, fuzzy sets and fuzzy logic. The upper-mentioned systems' functionality has been demonstrated on an inverted pendulum controlling problem. The three controllers have been designed for the controlling needs - the first one is on the basis of artificial neural networks, the second is a fuzzy one, and the third is based on ANFIS system. The thesis is aimed at comparing the described systems, which the controllers have been designed on the basis of, and evaluating the hybrid neuro-fuzzy system ANFIS contribution in comparison with particular theory solutions. Finally, some experiments with the systems are demonstrated and findings are assessed.

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Thompson, Richard. "Neuro-fuzzy predictive control of an information-poor system." Thesis, University of Oxford, 2002. http://ora.ox.ac.uk/objects/uuid:e463774c-a1c6-439e-a7e6-3cbb7aec68e3.

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While modern engineering systems have become increasingly integrated and complex over the years, interest in the application of control techniques which specifically attempt to formulate and solve the control problem in its inherently uncertain environment has been moderate, at best. More specifically, although many control schemes targeted at Heating, Ventilating and Air-Conditioning (HVAC) systems have been reported in the literature, most seem to rely on conventional techniques which assume that a detailed, precise model of the HVAC plant exists, and that the control objectives of the controller are clearly defined. Experience with HVAC systems shows that these assumptions are not always justifiable, and that, in practice, these systems are usually characterized by a lack of detailed design data and a lack of a robust understanding of the processes involved. Motivated by the need to more efficiently control complex, uncertain systems, this thesis focuses on the development and evaluation of a new neuro-fuzzy model-based predictive control scheme, where certain variables used in the optimization remain in the fuzzy domain. The method requires no training data from the actual plant under consideration, since detailed knowledge of the plant is unavailable. Results of the application of the control scheme to the control of thermal comfort in a simulated zone and to the control of the supply air temperature of an air-handling unit in the laboratory are presented. It is concluded that precious resources (as measured by actuator activity, for example) need not be wasted when controlling these systems. In addition, it is also shown that a very precise (and sometimes not necessarily accurate) control value computed at each sample is unnecessary. Rather, by defining the system and its environment in the fuzzy domain, the fuzzy decision algorithms developed here may be employed to get an "acceptable" control performance.

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Silva, Aldo Antonio Vieira da [UNESP]. "Desenvolvimento de aplicações em medicina e agronomia utilizando lógica fuzzy e neuro fuzzy." Universidade Estadual Paulista (UNESP), 2014. http://hdl.handle.net/11449/110517.

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Made available in DSpace on 2014-11-10T11:09:49Z (GMT). No. of bitstreams: 0 Previous issue date: 2014-02-28Bitstream added on 2014-11-10T11:58:00Z : No. of bitstreams: 1 000794270.pdf: 1454507 bytes, checksum: 21c1e569f66804233a47b876585652ce (MD5) O presente trabalho propõe duas novas metodologias de desenvolvimento: uma na área de medicina, no diagnóstico de hérnia inguinal utilizando a lógica fuzzy e outra, na área da agronomia, para estimação da produção de trigo utilizando o modelo de inferência adaptativo neuro fuzzy. Na primeira foi desenvolvido um aplicativo para dispositivos móveis, smartphones e tablets, auxiliando a tomada de decisão no diagnóstico de pacientes com suspeita de hérnia na região inguinal. Para isso, utilizou-se a linguagem JAVA juntamente com a biblioteca lógica fuzzy, denominada jfuzzylogic, e o sistema operacional Android para o desenvolvimento da aplicação. Para validar o aplicativo, utilizou-se a coleta de dados, via questionário, envolvendo 30 pacientes entrevistados em consulta médica. Como resultado, observou-se que o diagnóstico realizado pela equipe médica e o diagnóstico com o auxílio do aplicativo móvel, mostraram-se equivalentes nos casos dos pacientes acometidos com hérnia da região inguinal. Este software será disponibilizado gratuitamente, via web, para os profissionais da área da saúde. Já na segunda, investigou-se a habilidade de se desenvolver um modelo de inferência adaptativo neuro fuzzy para estimação da produtividade de trigo (Triticum aestivum) em função da adubação nitrogenada, com base em dados experimentais de cultivares de trigo, avaliada durante dois anos, em Selvíria-MS. Através dos dados de entrada e saída, o sistema de inferência neuro fuzzy adaptativo apreende e posteriormente pode estimar um novo valor de produção de trigo baseada em doses diferenciadas de nitrogênio. Os resultados mostraram que o sistema neuro fuzzy é viável para desenvolver um modelo de predição para estimar a produtividade de trigo em função da dose de nitrogênio. A produção estimada através do sitema neuro fuzzy proporcionou um erro RMSE (Raiz Quadrada do Erro Médio ... This work proposes two new application methods: one in the area of biomedical engineering in the diagnosis of inguinal hernias using fuzzy logic and another in the area of agriculture to estimate the wheat productivity using an adaptive neuro fuzzy inference system. The first was an application developed for mobile devices, smartphones and tablets, to assist decision making in the diagnosis of patients with suspected inguinal hernia. It was used the Java language together with the fuzzy logic library, denominated jfuzzylogic and the Android operating system for the application development. To validate the application it was used data obtained via questionnaire, involving 30 patients interviewed in medical consultation. As a result, it was observed that the diagnosis made by the medical team and diagnosis with the aid of the mobile application, were equivalent in cases of affected patients with hernia in the inguinal region. This software is available free of charge via the web, for professionals in the health field. In the second application method, it was investigated the ability to develop an adaptive neuro fuzzy inference system for estimating the productivity of wheat (Triticum aestivum) in relation to the nitrogen fertilization, based on experimental data of wheat cultivars during two years, in Selvíria-MS. Through the data input and output, the system of adaptive neuro fuzzy inference learns and subsequently can estimate a new value of wheat production based on different doses of nitrogen. The results showed that the neuro fuzzy system is feasible to develop a prediction model to estimate the productivity of wheat in relation to nitrogen rates. The RMSE (Root Mean Square Error) error of the estimated wheat productivity using the neuro fuzzy system was smaller than that obtained with the quadratic regression method, that is usually used in this kind of estimated, and also the relation between ...

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Conroy, Justin Anderson. "Analysis of adaptive neuro-fuzzy network structures." Thesis, Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/19684.

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Raad, Raad. "Neuro-fuzzy admission control in mobile communications systems." Access electronically, 2005. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20061030.153500/index.html.

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Silva, Aldo Antonio Vieira da. "Desenvolvimento de aplicações em medicina e agronomia utilizando lógica fuzzy e neuro fuzzy /." Ilha Solteira, 2014. http://hdl.handle.net/11449/110517.

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Orientador: Marcelo Carvalho Minhoto Teixeira Banca: Evaldo Assunção Banca: Rodrigo Cardim Banca: Cristiano Quevedo Andrea Banca: Ruy de Oliveira Resumo: O presente trabalho propõe duas novas metodologias de desenvolvimento: uma na área de medicina, no diagnóstico de hérnia inguinal utilizando a lógica fuzzy e outra, na área da agronomia, para estimação da produção de trigo utilizando o modelo de inferência adaptativo neuro fuzzy. Na primeira foi desenvolvido um aplicativo para dispositivos móveis, smartphones e tablets, auxiliando a tomada de decisão no diagnóstico de pacientes com suspeita de hérnia na região inguinal. Para isso, utilizou-se a linguagem JAVA juntamente com a biblioteca lógica fuzzy, denominada jfuzzylogic, e o sistema operacional Android para o desenvolvimento da aplicação. Para validar o aplicativo, utilizou-se a coleta de dados, via questionário, envolvendo 30 pacientes entrevistados em consulta médica. Como resultado, observou-se que o diagnóstico realizado pela equipe médica e o diagnóstico com o auxílio do aplicativo móvel, mostraram-se equivalentes nos casos dos pacientes acometidos com hérnia da região inguinal. Este software será disponibilizado gratuitamente, via web, para os profissionais da área da saúde. Já na segunda, investigou-se a habilidade de se desenvolver um modelo de inferência adaptativo neuro fuzzy para estimação da produtividade de trigo (Triticum aestivum) em função da adubação nitrogenada, com base em dados experimentais de cultivares de trigo, avaliada durante dois anos, em Selvíria-MS. Através dos dados de entrada e saída, o sistema de inferência neuro fuzzy adaptativo apreende e posteriormente pode estimar um novo valor de produção de trigo baseada em doses diferenciadas de nitrogênio. Os resultados mostraram que o sistema neuro fuzzy é viável para desenvolver um modelo de predição para estimar a produtividade de trigo em função da dose de nitrogênio. A produção estimada através do sitema neuro fuzzy proporcionou um erro RMSE (Raiz Quadrada do Erro Médio ... Abstract: This work proposes two new application methods: one in the area of biomedical engineering in the diagnosis of inguinal hernias using fuzzy logic and another in the area of agriculture to estimate the wheat productivity using an adaptive neuro fuzzy inference system. The first was an application developed for mobile devices, smartphones and tablets, to assist decision making in the diagnosis of patients with suspected inguinal hernia. It was used the Java language together with the fuzzy logic library, denominated jfuzzylogic and the Android operating system for the application development. To validate the application it was used data obtained via questionnaire, involving 30 patients interviewed in medical consultation. As a result, it was observed that the diagnosis made by the medical team and diagnosis with the aid of the mobile application, were equivalent in cases of affected patients with hernia in the inguinal region. This software is available free of charge via the web, for professionals in the health field. In the second application method, it was investigated the ability to develop an adaptive neuro fuzzy inference system for estimating the productivity of wheat (Triticum aestivum) in relation to the nitrogen fertilization, based on experimental data of wheat cultivars during two years, in Selvíria-MS. Through the data input and output, the system of adaptive neuro fuzzy inference learns and subsequently can estimate a new value of wheat production based on different doses of nitrogen. The results showed that the neuro fuzzy system is feasible to develop a prediction model to estimate the productivity of wheat in relation to nitrogen rates. The RMSE (Root Mean Square Error) error of the estimated wheat productivity using the neuro fuzzy system was smaller than that obtained with the quadratic regression method, that is usually used in this kind of estimated, and also the relation between ... Doutor

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NETO, LUIZ SABINO RIBEIRO. "ARTIFICIAL NEURAL NETWORKS, FUZZY LOGIC AND NEURO-FUZZY SYSTEM IN THE ROLE OF SHORT TERM LOAD FORECAST." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1999. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=7419@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO ELETROBRAS - CENTRAIS ELÉTRICAS BRASILEIRAS S. A. Esta dissertação investiga o desempenho de técnicas de inteligência computacional na previsão de carga em curto prazo. O objetivo deste trabalho foi propor e avaliar sistemas de redes neurais, lógica nebulosa, neuro-fuzzy e híbridos para previsão de carga em curto prazo, utilizando como entradas variáveis que influenciam o comportamento da carga, tais como: temperatura, índice de conforto e perfil de consumo. Este trabalho envolve 4 etapas principais: um estudo sobre previsão de carga e sobre as variáveis que influenciam o comportamento da carga; um estudo da aplicação de técnicas de inteligência computacional em previsão de carga; a definição de sistemas de redes neurais, lógica fuzzy e neuro-fuzzy em previsão de carga; e estudo de casos. No estudo sobre previsão de carga, foi observada a influência de algumas variáveis no comportamento da curva de carga de uma empresa de energia elétrica. Entre estas variáveis se encontram alguns dados meteorológicos (Temperatura, Umidade, Luminosidade, Índice de conforto, etc.), além de informações sobre o perfil de consumo de carga das empresas. Também foi observado o comportamento da série de carga com relação ao dia da semana, sua sazonalidade e a correlação entre o valor atual e valores passados. Foi realizado um levantamento bibliográfico sobre a aplicação de técnicas de inteligência computacional na previsão de carga. Os modelos de redes neurais, são os mais explorados até o momento. Os modelos de lógica fuzzy começaram a ser utilizados mais recentemente. Modelos neuro-fuzzy são mais recentes que os demais, não existindo portanto, muita bibliografia a respeito. Os projetos de aplicação dos três modelos foram classificados quanto à sua arquitetura, desempenho, erros medidos, entradas utilizadas e horizonte da previsão. Foram propostos e implementados 4 sistemas de previsão de carga: lógica fuzzy, redes neurais, sistema neuro-fuzzy hierárquico e um sistema híbrido neural/neuro- fuzzy. Os sistemas foram especializados para cada dia da semana, pelo fato do comportamento da carga ser distinto entre estes dias. Para os sistemas neural, neuro-fuzzy e híbrido os dados também foram separados em inverno e verão, pois o perfil de consumo de carga é diferente nestas estações. O sistema com lógica fuzzy foi modelado para realizar previsões de curtíssimo prazo (10 em 10 minutos), utilizando para isto o histórico de carga, hora do dia e intervalo de dez minutos dentro da hora do dia. As regras do sistema foram geradas automaticamente a partir do histórico de carga e os conjuntos nebulosos foram pré-definidos. O sistema com redes neurais teve sua arquitetura definida através de experimentos, utilizando- se apenas dados de carga, hora do dia e mês como entradas. O modelo de rede escolhido foi com retropropagação do erro (backpropagation). Foram realizados testes incluindo outras entradas como temperatura e perfil de consumo. Para o sistema neuro-fuzzy foi escolhido um sistema neuro-fuzzy hierárquico, que define automaticamente sua estrutura e as regras a partir do histórico dos dados. Em uma última etapa, foi estudado um sistema híbrido neural/ neuro- fuzzy, no qual a previsão da rede neural é uma entrada do sistema neuro-fuzzy. Para os três últimos modelos as previsões realizadas foram em curto prazo, com um horizonte de uma hora Os sistemas propostos foram testados em estudos de casos e os resultados comparados entre si e com os resultados obtidos em outros projetos na área. Os dados de carga utilizados no sistema com lógica fuzzy foram da CEMIG, no período de 1994 a 1996, em intervalos de 10 minutos, para previsões em curtíssimo prazo. Os resultados obtidos podem ser considerados bons em comparação com um sistema de redes neurais utilizando os mesmos dados. Para os demais modelos foram utilizados os seguintes dados: dados horários de carga da Light e da CPFL, no períod This thesis examines the performance of computational intelligence in short term load forecasting. The main objective of the work was to propose and evaluate neural network, fuzzy logic, neurofuzzy and hybrid systems in the role of short term load forecast, considering some variables that affect the load behavior such as temperature, comfort indexes and consumption profile. The work consisted in four main steps: a study about load forecasting; the modeling of neural network systems, fuzzy logic and neurofuzzy related to load forecast; and case studies. In the load forecasting studies, some variables appeared to affect the behavior of the load curve in the case of electrical utilities. These variables include meteorological data like temperature, humidity, lightening, comfort indexes etc, and also information about the consumption profile of the utilities. It was also noted the distinct behavior of the load series related to the day of the week, the seasonableness and the correlation between the past and present values. A bibliographic research concerning the application of computational intelligence techniques in load forecasting was made. This research showed that neural network models have been largely employed. The fuzzy logic models have just started to be used recently. Neuro-fuzzy are very recent, and there are almost no references on it. The surveyed application projects using the three models were classified by its architecture, performance, measured errors, inputs considered and horizon of the forecast. In this work four systems were proposed and implemented for load forecasting: fuzzy logic, neural network, hierarchical neuro-fuzzy and hybrid neural/neuro- fuzzy. The systems were specialized for each day of the week, due to the different behavior of the load found for each of the days. For the neural network, neuro-fuzzy and hybrid, the data were separated in winter and summer, due to the different behavior of the load in each of the seasons. The fuzzy logic system was modeled for very short term forecasting using the historic load for each hour of the day, in steps of 10 minutes within each hour. The fuzzy system rules were generated automatically based on the historic load and the fuzzy sets were pre-defined. The system with neural network had its architecture defined through experiments using only load data, hour of the day and month as input. The network model chosen was the back- propagation. Tests were performed adding other inputs such as temperature and consumption profile. For the neural- fuzzy, a hierarchical neuro-fuzzy system, which defines automatically its structure and rules based on the historical data, was employed. In a further step, a hybrid neural/neuro-fuzzy was studied, so as the neural network forecast is the input for the neuro-fuzzy system. For the last three models, short term forecasting was made for one hour period. The proposed systems were tested in case studies, and the results were compared themselves and with results obtained in other projects in the same area. The load data of CEMIG between 1994 and 1996 was used in the fuzzy logic system in steps of 10 minutes for very short term forecasting. The performance was good compared with a neural network system using the same data. For the other models, short term load forecasting (I hour, 24 steps ahead) was done using the following data: load data of LIGHT and CPFL between 1996 and 1998; temperature (hourly for LIGHT and daily for CPFL); the codification of month and hour of the day; and a profile of load by consumption class. For doing. The error results obtained by the models were around 1,15% for the fuzzy logic, 2,0% for the neural network, 1,5% for the neuro-fuzzy system, and 2,0% for the hybrid system. This work has showed the applicability of the computational intelligence techniques on load forecasting, demonstrating that a preliminary study of the series and their relation with

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Silva, Inara Aparecida Ferrer [UNESP]. "Aplicações de redes neurais e neuro fuzzy em engenharia biomédica e agronomia." Universidade Estadual Paulista (UNESP), 2014. http://hdl.handle.net/11449/110516.

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Made available in DSpace on 2014-11-10T11:09:48Z (GMT). No. of bitstreams: 0 Previous issue date: 2014-02-28Bitstream added on 2014-11-10T11:58:00Z : No. of bitstreams: 1 000794379.pdf: 1678626 bytes, checksum: b6d9b23c03cc8335775be33854ac5879 (MD5) Os sistemas fuzzy e neuro fuzzy têm sido usados com sucesso para resolver problemas em diversas áreas, como medicina, indústria, controle, agronomia e aplicações acadêmicas. Nas últimas décadas, as redes neurais têm sido utilizadas para identificação, avaliação e previsão e dados na medicina e na agronomia. Nesta tese, realizou-se um novo estudo comparativo entre as redes neuro fuzzy (ANFIS), rede perceptron multicamadas (MLP), rede função de base radial (RBF) e regressão generalizada (GRNN) na área de engenharia biomédica. Na engenharia biomédica as redes neurais e neuro fuzzy foram treinadas e validadas com dados de pacientes hígidos e hemiplégicos (pacientes com sequela motora após acidente vascular cerebral no hemicorpo direito ou esquerdo do cérebro) coletados por meio de um baropodômetro eletrônico (91 indivíduos, sendo 81 hígidos e 10 hemiplégicos). A rede GRNN apresentou o menor erro RMSE (Raiz Quadrada do Erro Médio Quadrático), porém a rede MLP conseguiu identificar um caso de hemiplegia. Na área de agricultura foi proposto um novo estudo comparativo utilizando redes neurais para previsão de produção de trigo (Triticum aestivum). Para este estudo utilizou-se uma base de dados experimental de trigo avaliada no período dois anos na região de Selvíria-MS. A validação foi realizada comparando-se a produção estimada pelas redes neurais MLP, GRNN e RBF com a curva de regressão quadrática, comumente utilizada para este fim, e com a rede neuro fuzzy ANFIS. O erro RMSE calculado com as redes neurais GRNN e RBF foi menor do que o obtido com a regressão quadrática e com o ANFIS utilizando o treinamento (híbrido). Para validação dos resultados obtidos em hemiplegia utilizou-se o RMSE, a matriz de confusão, a sensitividade, a especificidade e a acurácia. Os resultados mostraram que a utilização das redes neurais e redes neuro fuzzy, na engenharia biomédica, pode ser uma alternativa viável para ... The fuzzy and neuro fuzzy systems have been successfully used to solve problems in various fields such as medicine, manufacturing, control, agriculture and academic applications. In recent decades, neural networks have been used to the identification, assessment and diagnosis of diseases. In this thesis we performed a comparative study among fuzzy neural networks (ANFIS), multilayer perceptron neural networks (MLP), radial basis function network (RBF) and generalized regression (GRNN) in the area of biomedical engineering and agronomy. In biomedical engineering neural networks and neuro fuzzy were trained and validated with data set from patients (91 subjects, 81 healthy and 10 hemiplegic). The GRNN network had the lowest Root Mean Square Error (RMSE), but the MLP network was able to identify a case of hemiplegia. In the area of agriculture a comparative study to estimate the wheat (Triticum aestivum) productivity was proposed using neural networks. For this study it was used data from an experimental database of wheat cultivars evaluated during two years in the region of Selvíria - MS. The validation was performed by comparing the estimated productivity through the quadratic regression curve and the output of the ANFIS with the neural networks. The RMSE error calculated with the GRNN and RBF neural networks was lower than that obtained with the quadratic regression and the ANFIS. The results obtained in the study of hemiplegia were validated using the RMSE, the confusion matrix, the sensitivity, the specificity and the error accuracy. The results showed that the use of neural networks and fuzzy neural networks, in biomedical engineering, can be a viable for monitoring the progress of patients and discovery new information through a combination of parameters. In agriculture this methodology can bring benefits in combining several evaluation parameters of production to optimize production while minimize financial costs in new plantations

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Silva, Inara Aparecida Ferrer. "Aplicações de redes neurais e neuro fuzzy em engenharia biomédica e agronomia /." Ilha Solteira, 2014. http://hdl.handle.net/11449/110516.

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Orientador: Marcelo Carvalho Minhoto Teixeira Banca: Edvaldo Assunção Banca: Aparecido Augusto de Carvalho Banca: Cristiano Quevedo Andrea Banca: Valtemir Emerencio do Nascimento Resumo: Os sistemas fuzzy e neuro fuzzy têm sido usados com sucesso para resolver problemas em diversas áreas, como medicina, indústria, controle, agronomia e aplicações acadêmicas. Nas últimas décadas, as redes neurais têm sido utilizadas para identificação, avaliação e previsão e dados na medicina e na agronomia. Nesta tese, realizou-se um novo estudo comparativo entre as redes neuro fuzzy (ANFIS), rede perceptron multicamadas (MLP), rede função de base radial (RBF) e regressão generalizada (GRNN) na área de engenharia biomédica. Na engenharia biomédica as redes neurais e neuro fuzzy foram treinadas e validadas com dados de pacientes hígidos e hemiplégicos (pacientes com sequela motora após acidente vascular cerebral no hemicorpo direito ou esquerdo do cérebro) coletados por meio de um baropodômetro eletrônico (91 indivíduos, sendo 81 hígidos e 10 hemiplégicos). A rede GRNN apresentou o menor erro RMSE (Raiz Quadrada do Erro Médio Quadrático), porém a rede MLP conseguiu identificar um caso de hemiplegia. Na área de agricultura foi proposto um novo estudo comparativo utilizando redes neurais para previsão de produção de trigo (Triticum aestivum). Para este estudo utilizou-se uma base de dados experimental de trigo avaliada no período dois anos na região de Selvíria-MS. A validação foi realizada comparando-se a produção estimada pelas redes neurais MLP, GRNN e RBF com a curva de regressão quadrática, comumente utilizada para este fim, e com a rede neuro fuzzy ANFIS. O erro RMSE calculado com as redes neurais GRNN e RBF foi menor do que o obtido com a regressão quadrática e com o ANFIS utilizando o treinamento (híbrido). Para validação dos resultados obtidos em hemiplegia utilizou-se o RMSE, a matriz de confusão, a sensitividade, a especificidade e a acurácia. Os resultados mostraram que a utilização das redes neurais e redes neuro fuzzy, na engenharia biomédica, pode ser uma alternativa viável para ... Abstract: The fuzzy and neuro fuzzy systems have been successfully used to solve problems in various fields such as medicine, manufacturing, control, agriculture and academic applications. In recent decades, neural networks have been used to the identification, assessment and diagnosis of diseases. In this thesis we performed a comparative study among fuzzy neural networks (ANFIS), multilayer perceptron neural networks (MLP), radial basis function network (RBF) and generalized regression (GRNN) in the area of biomedical engineering and agronomy. In biomedical engineering neural networks and neuro fuzzy were trained and validated with data set from patients (91 subjects, 81 healthy and 10 hemiplegic). The GRNN network had the lowest Root Mean Square Error (RMSE), but the MLP network was able to identify a case of hemiplegia. In the area of agriculture a comparative study to estimate the wheat (Triticum aestivum) productivity was proposed using neural networks. For this study it was used data from an experimental database of wheat cultivars evaluated during two years in the region of Selvíria - MS. The validation was performed by comparing the estimated productivity through the quadratic regression curve and the output of the ANFIS with the neural networks. The RMSE error calculated with the GRNN and RBF neural networks was lower than that obtained with the quadratic regression and the ANFIS. The results obtained in the study of hemiplegia were validated using the RMSE, the confusion matrix, the sensitivity, the specificity and the error accuracy. The results showed that the use of neural networks and fuzzy neural networks, in biomedical engineering, can be a viable for monitoring the progress of patients and discovery new information through a combination of parameters. In agriculture this methodology can bring benefits in combining several evaluation parameters of production to optimize production while minimize financial costs in new plantations Doutor

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Funsten, Brad Thomas Mr. "ECG Classification with an Adaptive Neuro-Fuzzy Inference System." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1380.

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Heart signals allow for a comprehensive analysis of the heart. Electrocardiography (ECG or EKG) uses electrodes to measure the electrical activity of the heart. Extracting ECG signals is a non-invasive process that opens the door to new possibilities for the application of advanced signal processing and data analysis techniques in the diagnosis of heart diseases. With the help of today’s large database of ECG signals, a computationally intelligent system can learn and take the place of a cardiologist. Detection of various abnormalities in the patient’s heart to identify various heart diseases can be made through an Adaptive Neuro-Fuzzy Inference System (ANFIS) preprocessed by subtractive clustering. Six types of heartbeats are classified: normal sinus rhythm, premature ventricular contraction (PVC), atrial premature contraction (APC), left bundle branch block (LBBB), right bundle branch block (RBBB), and paced beats. The goal is to detect important characteristics of an ECG signal to determine if the patient’s heartbeat is normal or irregular. The results from three trials indicate an average accuracy of 98.10%, average sensitivity of 94.99%, and average specificity of 98.87%. These results are comparable to two artificial neural network (ANN) algorithms: gradient descent and Levenberg Marquardt, as well as the ANFIS preprocessed by grid partitioning.

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Chaves, Luciano Eustáquio [UNESP]. "Modelos computacionais fuzzy e neuro-fuzzy para avaliarem os efeitos da poluição do ar." Universidade Estadual Paulista (UNESP), 2013. http://hdl.handle.net/11449/105352.

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Made available in DSpace on 2014-06-11T19:34:58Z (GMT). No. of bitstreams: 0 Previous issue date: 2013-07-26Bitstream added on 2014-06-13T19:44:42Z : No. of bitstreams: 1 chaves_le_dr_guara.pdf: 1947123 bytes, checksum: ee8c45d5619f8378be62c209cfe77f98 (MD5) O presente estudo teve por objetivo verificar a associação entre a exposição aos poluentes do ar e o número de internações hospitalares por asma e pneumonia. Para a verificação foi proposto desenvolver e validar modelos fuzzy (Mamdani) e neuro-fuzzy (Sugeno) e comparar qual dos modelos apresenta uma melhor eficácia para a predição de internações. A metodologia utilizada foi dividida em três módulos: limpeza e elaboração de dados, elaboração do modelo fuzzy (Mamdani) e elaboração do modelo neuro-fuzzy (Sugeno). Foram coletados dados reais de internações do DATASUS, os quais foram utilizados como saída do modelo. Os dados de entradas foram os poluentes do ar material particulado (MP10), dióxido de enxofre (SO2), ozônio (O3) e a temperatura aparente (Tap). As saídas geradas pelos modelos foram comparadas e correlacionadas com os dados reais de internações através do Coeficiente de Correlação de Pearson. Para o estudo o nível de significância estatístico adotado foi α = 5%. A acurácia dos modelos foi realizada utilizando a Curva ROC. Neste estudo foi possível desenvolver e validar os modelos. O modelo neuro-fuzzy apresentou melhor correlação do que o modelo fuzzy; porém a acurácia foi melhor para o modelo fuzzy This study aimed at investigating the association between exposure to air pollutants and the number of hospital admissions for asthma and pneumonia. For such verification it was proposed to develop and validate the Mamdani fuzzy and neuro-fuzzy (Sugeno) models and compare which of the two provides better efficacy in predicting hospitalization. The methodology was divided into three modules: data cleaning and preparation, elaboration of the fuzzy model (Mamdani) and elaboration of the neuro-fuzzy model (Sugeno). Data were collected from DATASUS actual admissions, which were used as the models output. The input data were air pollutants particulate matter (PM10), sulfur dioxide (SO2), ozone (O3) and the apparent temperature (Tap). The outputs generated by the models were compared and correlated with the actual data of admissions through the Pearson Correlation Coefficient. In this study the level of statistical significance adopted was α = 5%. The accuracy of the models was performed using the ROC curve. In this study it was possible to develop and validate the models. The neuro-fuzzy model showed better correlation than the fuzzy model, but the accuracy was better for the fuzzy model

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Chaves, Luciano Eustáquio 1973. "Modelos computacionais fuzzy e neuro-fuzzy para avaliarem os efeitos da poluição do ar /." Guaratinguetá, 2013. http://hdl.handle.net/11449/105352.

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Orientador: Luiz fernando Costa Nascimento Coorientador: Paloma Maria Silva Rocha Rizol Banca: Leonardo Mesquita Banca: Andrea Paula Peneluppi de Medeiros Banca: Maria Helena Baena de Moraes Lopes Banca: Marcos Arbex Resumo: O presente estudo teve por objetivo verificar a associação entre a exposição aos poluentes do ar e o número de internações hospitalares por asma e pneumonia. Para a verificação foi proposto desenvolver e validar modelos fuzzy (Mamdani) e neuro-fuzzy (Sugeno) e comparar qual dos modelos apresenta uma melhor eficácia para a predição de internações. A metodologia utilizada foi dividida em três módulos: limpeza e elaboração de dados, elaboração do modelo fuzzy (Mamdani) e elaboração do modelo neuro-fuzzy (Sugeno). Foram coletados dados reais de internações do DATASUS, os quais foram utilizados como saída do modelo. Os dados de entradas foram os poluentes do ar material particulado (MP10), dióxido de enxofre (SO2), ozônio (O3) e a temperatura aparente (Tap). As saídas geradas pelos modelos foram comparadas e correlacionadas com os dados reais de internações através do Coeficiente de Correlação de Pearson. Para o estudo o nível de significância estatístico adotado foi α = 5%. A acurácia dos modelos foi realizada utilizando a Curva ROC. Neste estudo foi possível desenvolver e validar os modelos. O modelo neuro-fuzzy apresentou melhor correlação do que o modelo fuzzy; porém a acurácia foi melhor para o modelo fuzzy Abstract: This study aimed at investigating the association between exposure to air pollutants and the number of hospital admissions for asthma and pneumonia. For such verification it was proposed to develop and validate the Mamdani fuzzy and neuro-fuzzy (Sugeno) models and compare which of the two provides better efficacy in predicting hospitalization. The methodology was divided into three modules: data cleaning and preparation, elaboration of the fuzzy model (Mamdani) and elaboration of the neuro-fuzzy model (Sugeno). Data were collected from DATASUS actual admissions, which were used as the models output. The input data were air pollutants particulate matter (PM10), sulfur dioxide (SO2), ozone (O3) and the apparent temperature (Tap). The outputs generated by the models were compared and correlated with the actual data of admissions through the Pearson Correlation Coefficient. In this study the level of statistical significance adopted was α = 5%. The accuracy of the models was performed using the ROC curve. In this study it was possible to develop and validate the models. The neuro-fuzzy model showed better correlation than the fuzzy model, but the accuracy was better for the fuzzy model Doutor

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Wang, Liren. "An approach to neuro-fuzzy feedback control in statistical process control." Thesis, University of South Wales, 2001. https://pure.southwales.ac.uk/en/studentthesis/an-approach-to-neurofuzzy-feedback-control-in-statistical-process-control(7d9c736f-e85d-4873-a6bb-9bcea107d371).html.

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It is a difficult challenge to develop a feedback control system for Statistical Process Control (SPC) because there is no effective method that can be used to calculate the accurate magnitude of feedback control actions in traditional SPC. Suitable feedback adjustments are generated from the experiences of process engineers. This drawback means that the SPC technique can not be directly applied in an automatic system. This thesis is concerned with Fuzzy Sets and Fuzzy Logic applied to the uncertainty of relationships between the SPC (early stage) alarms and SPC implementation. Based on a number of experiments of the frequency distribution for shifts of abnormal process averages and human subjective decision, a Fuzzy-SPC control system is developed to generate the magnitude of feedback control actions using fuzzy inference. A simulation study which is written in C++ is designed to implement a Fuzzy-SPC controller with satisfactory results. To further reduce the control errors, a NeuroFuzzy network is employed to build NNFuzzy- SPC system in MATLAB. The advantage of the leaning capability of Neural Networks is used to optimise the parameters of the Fuzzy- X and Fuzzy-J? controllers in order to obtain the ideal consequent membership functions to adapt to the randomness of various processes. Simulation results show that the NN-Fuzzy-SPC control system has high control accuracy and stable repeatability. To further improve the practicability of a NN-Fuzzy-SPC system, a combined forecaster with EWMA chart and digital filter is designed to reduce the NN-Fuzzy-SPC control delay. For the EWMA chart, the smoothing constant 0 is investigated by a number of experiments and optimised in the forecast process. The Finite Impulse Response (FIR) lowpass filter is designed to smooth the input data (signal) fluctuations in order to reduce the forecast errors. An improved NN-Fuzzy-SPC control system which shows high control accuracy and short control delay can be applied in both automatic control and online quality control.

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Killing, Jonathan. "Design and development of an intelligent neuro-fuzzy system for automated visual inspection." Thesis, Kingston, Ont. : [s.n.], 2007. http://hdl.handle.net/1974/443.

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Hanlon, Nicholas P. "Neuro-Fuzzy Dynamic Programming for Decision-Making and Resource Allocation during Wildland Fires." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1321370261.

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Shook, David Adam. "Control of a benchmark structure using GA-optimized fuzzy logic control." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1088.

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França, Daniel cruz de. "Modelagem de um adaptive neuro fuzzy inference system para análise de risco em projetos." Universidade Federal da Paraíba, 2016. http://tede.biblioteca.ufpb.br:8080/handle/tede/8163.

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Submitted by Maike Costa (maiksebas@gmail.com) on 2016-04-29T13:48:06Z No. of bitstreams: 1 arquivo total.pdf: 1906817 bytes, checksum: 6bf3c54782cfdea75b86311d9bc28cb9 (MD5) Made available in DSpace on 2016-04-29T13:48:06Z (GMT). No. of bitstreams: 1 arquivo total.pdf: 1906817 bytes, checksum: 6bf3c54782cfdea75b86311d9bc28cb9 (MD5) Previous issue date: 2016-02-22 Several researches highlight the importance of risk management in project management. Many authors propose traditional models with statistical and deterministic methods, though some risk project management issues are based on conceptual frameworks, expert opinion and human experience. This kind of problem makes difficult the use of classical models, but can be mathematically treated using fuzzy logic. In addition, historical data of projects can provide information about the organization's risk analysis experience and be modelled by a learning mechanism. The method used in this work is the Adaptive Neuro-fuzzy Inference System (ANFIS), which is capable of aggregating the mathematical treatment capacity of conceptual models with a hybrid learning algorithm. Thus, the aim of this study is to model an ANFIS that is able to analyze the risks of projects. A set of projects was analyzed by means of a risk management checklist with factors arranged in a risk breakdown structure (RBS). Estimates were made using probability and impact matrix, and expert opinion. The risk of each project was defined as an integer between 1 and 10. To select the best model among 32 different ANFIS settings, 84% of the data were used in 10-fold cross-validation. The model with the best results in validation process was selected and tested with the remaining data. The results attained in the evaluation were: mean squared error (MSE) of 0.2207, mean absolute error (MAE) of 0.3084, coefficient of determination (R²) of 0.9733 and 80% of accuracy. These results indicate that the project risk management can be successfully performed by ANFIS. This enables the modeling of knowledge and human experience and can reduce costs of skilled labor and improve the speed of analysis. Diversas pesquisas ressaltam a importância do gerenciamento de risco na gestão de projetos. Muitos autores propõem modelos tradicionais com métodos estatísticos ou determinísticos, entretanto alguns problemas de gerenciamento de risco em projetos são baseados em estruturas conceituais, na opinião especializada e na experiência humana. Esse tipo de problema dificulta a utilização de modelos clássicos, mas pode ser tratado matematicamente por meio da lógica fuzzy. Além disso, dados históricos de projetos podem fornecer informações sobre a experiência de analise de risco da organização e ser modelados por mecanismo de aprendizagem. O mecanismo utilizado nesse trabalho é o Adaptive Neuro-fuzzy Inferece System (ANFIS), que é capaz de agregar a capacidade de tratamento matemático de modelos conceituais com um algoritmo de aprendizagem híbrido. Desse modo, o objetivo desse trabalho é modelar um Adaptive Neuro-fuzzy Inferece System capaz de analisar os riscos de projetos. Um conjunto de projetos foi analisado por meio de uma lista de verificação com fatores de risco organizados em uma estrutura analítica de risco (EAR). As estimativas foram realizadas por meio de matrizes de probabilidade e impacto e opinião especializada. O risco de cada projeto foi definido como um número inteiro entre 1 e 10. Foram utilizados 84% dados na validação cruzada 10-fold para seleção do melhor modelo entre 32 diferentes configurações de ANFIS. O modelo com os melhores resultados de validação foi selecionado e testado com os dados restantes. Os resultados alcançados na avaliação foram: erro quadrático médio (MSE) de 0,2207, erro absoluto médio de 0,3084, coeficiente de determinação (R²) de 0,9733 e acurácia de 80%. Esses resultados indicam que o gerenciamento de riscos em projetos pode ser realizado com sucesso através do ANFIS. Isso possibilita a modelagem de conhecimento e experiências humanas e pode diminuir custos com mão de obra especializada e aumentar a velocidade das análises.

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Maduko, Elizabeth. "Development and testing of a neuro-fuzzy classification system for IOS data in asthmatic children." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2007. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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Shekarriz, Mona. "The foundation of capability modelling : a study of the impact and utilisation of human resources." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/5257.

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This research aims at finding a foundation for assessment of capabilities and applying the concept in a human resource selection. The research identifies a common ground for assessing individuals’ applied capability in a given job based on literature review of various disciplines in engineering, human sciences and economics. A set of criteria is found to be common and appropriate to be used as the basis of this assessment. Applied Capability is then described in this research as the impact of the person in fulfilling job requirements and also their level of usage from their resources with regards to the identified criteria. In other words how their available resources (abilities, skills, value sets, personal attributes and previous performance records) can be used in completing a job. Translation of the person’s resources and task requirements using the proposed criteria is done through a novel algorithm and two prevalent statistical inference techniques (OLS regression and Fuzzy) are used to estimate quantitative levels of impact and utilisation. A survey on post graduate students is conducted to estimate their applied capabilities in a given job. Moreover, expert academics are surveyed on their views on key applied capability assessment criteria, and how different levels of match between job requirement and person’s resources in those criteria might affect the impact levels. The results from both surveys were mathematically modelled and the predictive ability of the conceptual and mathematical developments were compared and further contrasted with the observed data. The models were tested for robustness using experimental data and the results for both estimation methods in both surveys are close to one another with the regression models being closer to observations. It is believed that this research has provided sound conceptual and mathematical platforms which can satisfactorily predict individuals’ applied capability in a given job. This research has contributed to the current knowledge and practice by a) providing a comparison of capability definitions and uses in different disciplines, b) defining criteria for applied capability assessment, c) developing an algorithm to capture applied capabilities, d) quantification of an existing parallel model and finally e) estimating impact and utilisation indices using mathematical methods.

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Chen, Chen. "Soft Computing-based Life-Cycle Cost Analysis Tools for Transportation Infrastructure Management." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/28214.

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Increasing demands, shrinking financial and human resources, and increased infrastructure deterioration have made the task of maintaining the infrastructure systems more challenging than ever before. Life-cycle cost analysis (LCCA) is an important tool for transportation infrastructure management, which is used extensively to support project level decisions, and is increasingly being applied to enhance network level analysis. However, traditional LCCA tools cannot practically and effectively utilize expert knowledge and handle ambiguous uncertainties. The main objective of this dissertation was to develop enhanced LCCA models using soft computing (mainly fuzzy logic) techniques. The proposed models use available "real-world" information to forecast life-cycle costs of competing maintenance and rehabilitation strategies and support infrastructure management decisions. A critical review of available soft computing techniques and their applications in infrastructure management suggested that these techniques provide appealing alternatives for supporting many of the infrastructure management functions. In particular, LCCA often utilizes information that is uncertain, ambiguous and incomplete, which is obtained from both existing databases and expert opinion. Consequently, fuzzy logic techniques were selected to enhance life-cycle cost analysis of transportation infrastructure investments because they provide a formal approach for the effective treatment of these types of information. The dissertation first proposes a fuzzy-logic-based decision-support model, whose inference rules can be customized according to agency's management policies and expert opinion. The feasibility and practicality of the proposed model is illustrated by its implementation in a life-cycle cost analysis algorithm for comparing and selecting pavement maintenance, rehabilitation and reconstruction (MR&R) policies. To enhance the traditional probabilistic LCCA model, the fuzzy-logic-based model is then incorporated into the risk analysis process. A fuzzy logic approach for determining the timing of pavement MR&R treatments in a probabilistic LCCA model for selecting pavement MR&R strategies is proposed. The proposed approach uses performance curves and fuzzy-logic triggering models to determine the most effective timing of pavement MR&R activities. The application of the approach in a case study demonstrates that the fuzzy-logic-based risk analysis model for LCCA can effectively produce results that are at least comparable to those of the benchmark methods while effectively considering some of the ambiguous uncertainty inherent to the process. Finally, the research establishes a systematic method to calibrate the fuzzy-logic based rehabilitation decision model using real cases extracted from the Long Term Pavement Performance (LTPP) database. By reinterpreting the model in the form of a neuro-fuzzy system, the calibration algorithm takes advantage of the learning capabilities of artificial neural networks for tuning the fuzzy membership functions and rules. The practicality of the method is demonstrated by successfully tuning the treatment selection model to distinguish between rehabilitation (light overlay) and do-nothing cases. Ph. D.

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Lima, Fábio. "Estimador neuro-fuzzy de velocidade aplicado ao controle vetorial sem sensores de motores de indução trifásicos." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/3/3143/tde-20092011-150232/.

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Este trabalho apresenta uma alternativa ao controle vetorial de motores de indução, sem a utilização de sensores para realimentação da velocidade mecânica do motor. Ao longo do tempo, diversas técnicas de controle vetorial têm sido propostas na literatura. Dentre elas está a técnica de controle por orientação de campo (FOC), muito utilizada na indústria e presente também neste trabalho. A principal desvantagem do FOC é a sua grande sensibilidade às variações paramétricas da máquina, as quais podem invalidar o modelo e as ações de controle. Nesse sentido, uma estimativa correta dos parâmetros da máquina, torna-se fundamental para o acionamento. Este trabalho propõe o desenvolvimento e implementação de um estimador baseado em um sistema de inferência neuro-fuzzy adaptativo (ANFIS) para o controle de velocidade do motor de indução trifásico em um acionamento sem sensores. Pelo fato do acionamento em malha fechada admitir diversas velocidades de regime estacionário para o motor, uma nova metodologia de treinamento por partição de frequência é proposta. Ainda, faz-se a validação do sistema utilizando a orientação de campo magnético no referencial de campo de entreferro da máquina. Simulações para avaliação do desempenho do estimador mediante o acionamento vetorial do motor foram realizadas utilizando o programa Matlab/Simulink. Para a validação prática do modelo, uma bancada de testes foi implementada; o acionamento do motor foi realizado por um inversor de frequência do tipo fonte de tensão (VSI) e o controle vetorial, incluindo o estimador neuro-fuzzy, foi realizado pelo pacote de tempo real do programa Matlab/Simulink, juntamente com uma placa de aquisição de dados da National Instruments. This work presents an alternative sensorless vector control of induction motors. Several techniques for induction motor control have been proposed in the literature. Among these is the field oriented control (FOC), strongly used in industries and also in this work. The main drawback of the FOC technique is its sensibility to deviations of the parameters of the machine, which can deteriorate the control actions. Therefore, an accurate determination of the machines parameters is mandatory to the drive system. This work proposes the development of an adaptive neuro-fuzzy inference system (ANFIS) estimator to control the angular speed of a three-phase induction motor in a sensorless drive. In a closed loop configuration, several speed commands can be imposed to the motor. Thus, a new frequency partition training of ANFIS is proposed. Moreover, the ANFIS speed estimator is validated in a magnetizing flux oriented control scheme. Simulations to evaluate the performance of the estimator considering the vector drive system were done by the Matlab/Simulink. To determine the benefits of the proposed model a practical system was implemented using a voltage source inverter (VSI) and the vector control including the ANFIS estimator, carried out by the Real Time Toolbox from Matlab/Simulink and a data acquisition card from National Instruments.

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Ocampo, Duque William Andrés. "On the development of decision-making systems based on fuzzy models to assess water quality in rivers." Doctoral thesis, Universitat Rovira i Virgili, 2008. http://hdl.handle.net/10803/8566.

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There are many situations where a linguistic description of complex phenomena allows better assessments. It is well known that the assessment of water quality continues depending heavily upon subjective judgments and interpretation, despite the huge datasets available nowadays. In that sense, the aim of this study has been to introduce intelligent linguistic operations to analyze databases, and produce self interpretable water quality indicators, which tolerate both imprecision and linguistic uncertainty. Such imprecision typically reflects the ambiguity of human thinking when perceptions need to be expressed. Environmental management concepts such as: "water quality", "level of risk", or "ecological status" are ideally dealt with linguistic variables. In the present Thesis, the flexibility of computing with words offered by fuzzy logic has been considered in these management issues. Firstly, a multipurpose hierarchical water quality index has been designed with fuzzy reasoning. It integrates a wide set of indicators including: organic pollution, nutrients, pathogens, physicochemical macro-variables, and priority micro-contaminants. Likewise, the relative importance of the water quality indicators has been dealt with the analytic hierarchy process, a decision-aiding method. Secondly, a methodology based on a hybrid approach that combines fuzzy inference systems and artificial neural networks has been used to classify ecological status in surface waters according to the Water Framework Directive. This methodology has allowed dealing efficiently with the non-linearity and subjective nature of variables involved in this classification problem. The complexity of inference systems, the appropriate choice of linguistic rules, and the influence of the functions that transform numerical variables into linguistic variables have been studied. Thirdly, a concurrent neuro-fuzzy model based on screening ecological risk assessment has been developed. It has considered the presence of hazardous substances in rivers, and incorporates an innovative ranking and scoring system, based on a self-organizing map, to account for the likely ecological hazards posed by the presence of chemical substances in freshwater ecosystems. Hazard factors are combined with environmental concentrations within fuzzy inference systems to compute ecological risk potentials under linguistic uncertainty. The estimation of ecological risk potentials allows identifying those substances requiring stricter controls and further rigorous risk assessment. Likewise, the aggregation of ecological risk potentials, by means of empirical cumulative distribution functions, has allowed estimating changes in water quality over time. The neuro-fuzzy approach has been validated by comparison with biological monitoring. Finally, a hierarchical fuzzy inference system to deal with sediment based ecological risk assessment has been designed. The study was centered in sediments, since they produce complementary findings to water quality analysis, especially when temporal trends are required. Results from chemical and eco-toxicological analyses have been used as inputs to two parallel inference systems which assess levels of contamination and toxicity, respectively. Results from both inference engines are then treated in a third inference engine which provides a final risk characterization, where the risk is provided in linguistic terms, with their respective degrees of certitude. Inputs to the risk system have been the levels of potentially toxic substances, mainly metals and chlorinated organic compounds, and the toxicity measured with a screening test which uses the photo-luminescent bacteria Vibrio fischeri. The Ebro river basin has been selected as case study, although the methodologies here explained can easily be applied to other rivers. In conclusion, this study has broadly demonstrated that the design of water quality indexes, based on fuzzy logic, emerges as suitable and alternative tool to support decision makers involved in effective sustainable river basin management plans. Existen diversas situaciones en las cuales la descripción en términos lingüísticos de fenómenos complejos permite mejores resultados. A pesar de los volúmenes de información cuantitativa que se manejan actualmente, es bien sabido que la gestión de la calidad del agua todavía obedece a juicios subjetivos y de interpretación de los expertos. Por tanto, el reto en este trabajo ha sido la introducción de operaciones lógicas que computen con palabras durante el análisis de los datos, para la elaboración de indicadores auto-interpretables de calidad del agua, que toleren la imprecisión e incertidumbre lingüística. Esta imprecisión típicamente refleja la ambigüedad del pensamiento humano para expresar percepciones. De allí que las variables lingüísticas se presenten como muy atractivas para el manejo de conceptos de la gestión medioambiental, como es el caso de la "calidad del agua", el "nivel de riesgo" o el "estado ecológico". Por tanto, en la presente Tesis, la flexibilidad de la lógica difusa para computar con palabras se ha adaptado a diversos tópicos en la gestión de la calidad del agua. Primero, se desarrolló un índice jerárquico multipropósito de calidad del agua que se obtuvo mediante razonamiento difuso. El índice integra un extenso grupo de indicadores que incluyen: contaminación orgánica, nutrientes, patógenos, variables macroscópicas, así como sustancias prioritarias micro-contaminantes. La importancia relativa de los indicadores al interior del sistema de inferencia se estimó con un método de análisis de decisiones, llamado proceso jerárquico analítico. En una segunda fase, se utilizó una metodología híbrida que combina los sistemas de inferencia difusos y las redes neuronales artificiales, conocida como neuro-fuzzy, para el estudio de la clasificación del estado ecológico de los ríos, de acuerdo con los lineamientos de la Directiva Marco de Aguas. Esta metodología permitió un manejo adecuado de la no-linealidad y naturaleza subjetiva de las variables involucradas en este problema clasificatorio. Con ella, se estudió la complejidad de los sistemas de inferencia, la selección apropiada de reglas lingüísticas y la influencia de las funciones que transforman las variables numéricas en lingüísticas. En una tercera fase, se desarrolló un modelo conceptual neuro-fuzzy concurrente basado en la metodología de evaluación de riesgo ecológico preliminar. Este modelo consideró la presencia de sustancias peligrosas en los ríos, e incorporó un mapa auto-organizativo para clasificar las sustancias químicas, en términos de su peligrosidad hacia los ecosistemas acuáticos. Con este modelo se estimaron potenciales de riesgo ecológico por combinación de factores de peligrosidad y de concentraciones de las sustancias químicas en el agua. Debido a la alta imprecisión e incertidumbre lingüística, estos potenciales se obtuvieron mediante sistemas de inferencia difusos, y se integraron por medio de distribuciones empíricas acumuladas, con las cuales se pueden analizar cambios espacio-temporales en la calidad del agua. Finalmente, se diseñó un sistema jerárquico de inferencia difuso para la evaluación del riesgo ecológico en sedimentos de ribera. Este sistema estima los grados de contaminación, toxicidad y riesgo en los sedimentos en términos lingüísticos, con sus respectivos niveles de certeza. El sistema se alimenta con información proveniente de análisis químicos, que detectan la presencia de sustancias micro-contaminantes, y de ensayos eco-toxicológicos tipo "screening" que usan la bacteria Vibrio fischeri. Como caso de estudio se seleccionó la cuenca del río Ebro, aunque las metodologías aquí desarrolladas pueden aplicarse fácilmente a otros ríos. En conclusión, este trabajo demuestra ampliamente que el diseño y aplicación de indicadores de calidad de las aguas, basados en la metodología de la lógica difusa, constituyen una herramienta sencilla y útil para los tomadores de decisiones encargados de la gestión sostenible de las cuencas hidrográficas.

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Sanches, Heleno da Luz Monteiro. "Optimização do despacho e reserva girante em sistemas eléctricos híbridos. Estudo de caso: sistema eléctrico da Ilha de Santiago em Cabo Verde." Master's thesis, Faculdade de Ciências e Tecnologia, 2012. http://hdl.handle.net/10362/8737.

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Dissertação para obtenção do grau de Mestre em Energias Renováveis – Conversão Eléctrica e Utilização Sustentáveis Com os avanços conseguidos no campo de tecnologias de conversão de energias renováveis nos últimos 20 anos, e as escaladas no preço do petróleo dos últimos anos, tornou-se mais atractivo investir em tecnologias de conversão de energias renováveis, principalmente em sistemas eléctricos isolados de elevada disponibilidade de recursos renováveis, como é o caso do sistema eléctrico da ilha de Santiago em Cabo Verde, onde aumentou-se consideravelmente a penetração renovável nos últimos três anos. Contudo, sobretudo devido à variabilidade dos recursos e produção renovável, o aumento destas fontes nos sistemas eléctricos isolados acrescenta também desafios à tomada de decisão de optimização do despacho e reserva girante. Assim, é apresentado nesta dissertação um sistema inteligente que se baseia na lógica difusa (fuzzy logic) e sistema neuro-fuzzy (ANFIS) para optimizar automaticamente o despacho e reserva girante no Sistema Eléctrico Híbrido da Ilha de Santiago (SEHIS). O sistema proposto baseia-se na previsão do consumo e produção renovável, nomeadamente a produção eólica e fotovoltaica, e despacha automaticamente os geradores a fuelóleo com base nos seus custos de produção, por forma a permitir a máxima penetração renovável, reduzindo assim o consumo do fuelóleo e, consequentemente, o custo de produção. Além disso, o sistema proposto salvaguarda as restrições técnicas do sistema eléctrico, nomeadamente a reserva girante mínima necessária para fazer face à contingência ou erro de previsão, e ainda as restrições técnicas dos geradores, designadamente o limite mínimo de carga recomendado pelos fabricantes (50%), permitindo desta forma evitar a degradação da eficiência e aumento de avarias dos geradores.

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Aslan, Muhittin. "Modeling The Water Quality Of Lake Eymir Using Artificial Neural Networks (ann) And Adaptive Neuro Fuzzy Inference System (anfis)." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12610211/index.pdf.

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Lakes present in arid regions of Central Anatolia need further attention with regard to water quality. In most cases, mathematical modeling is a helpful tool that might be used to predict the DO concentration of a lake. Deterministic models are frequently used to describe the system behavior. However most ecological systems are so complex and unstable. In case, the deterministic models have high chance of failure due to absence of priori information. For such cases black box models might be essential. In this study DO in Eymir Lake located in Ankara was modeled by using both Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). Phosphate, Orthophospate, pH, Chlorophyll-a, Temperature, Alkalinity, Nitrate, Total Kjeldahl Nitrogen, Wind, Precipitation, Air Temperature were the input parameters of ANN and ANFIS. The aims of these modeling studies were: to develop models with ANN to predict DO concentration in Lake Eymir with high fidelity to actual DO data, to compare the success (prediction capacity) of ANN and ANFIS on DO modeling, to determine the degree of dependence of different parameters on DO. For modeling studies &ldquo Matlab R 2007b&rdquo software was used. The results indicated that ANN has high prediction capacity of DO and ANFIS has low with respect to ANN. Failure of ANFIS was due to low functionality of Matlab ANFIS Graphical User Interface. For ANN Modeling effect of meteorological data on DO data on surface of the lake was successfully described and summer month super saturation DO concentrations were successfully predicted.

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Gil, Mauro Cesar Cantarino. "Aplicação de redes neuro-fuzzy para a solução de problemas inversos em transferência radiativa." Universidade do Estado do Rio de Janeiro, 2010. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=1605.

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Nesta tese é proposta uma implementação para a solução do problema inverso com as estimativas das propriedades radiativas (o albedo de espalhamento simples, a espessura ótica do meio e as reflectividades difusas) a partir dos valores das intensidades de radiação que deixam o meio participante utilizando uma abordagem híbrida de sistemas neuro-fuzzy (SNF), o qual combina a utilização de sistemas de inferência fuzzy com as redes neurais artificiais. Busca-se com a utilização desse sistema híbrido integrar a habilidade dos sistemas fuzzy no tratamento de informações inexatas, imprecisas, e vagas, e a capacidade das redes neurais artificiais de tratar o aprendizado por experiência e a generalização do conhecimento. É proposta também uma metodologia de máquinas de comitês neuro-fuzzy na solução deste problema inverso em transferência radiativa. Foi observado paralelamente que a solução dos sistemas neuro-fuzzy e dos sistemas híbridos de máquinas de comitê neuro-fuzzy, apresentam baixa qualidade nos resultados quando são utilizados os dados experimentais com os menores coeficientes de sensibilidade para os parâmetros que serão estimados. Por outro lado, quando são utilizados dados com maior sensibilidade, são obtidos melhores resultados. Esta abordagem procura evitar a possibilidade da não convergência desses métodos. In this thesis is proposed an implementation for solving the inverse problem with the estimates of radiative properties (the single scattering albedo, the optical thickness of the media and the diffuse reflectivities) by the values of the intensities of radiation that leaves the participant medium using a hybrid approach of neuro-fuzzy systems, which combines the use of fuzzy inference systems with artificial neural networks. The use of this hybrid system try to include the ability of fuzzy systems in the treatment of inaccurate, imprecise, and vague data, and the ability of artificial neural networks to deal with learning from experience and widespread knowledge. Also is proposed a methodology for machines committees in neuro-fuzzy solution of this inverse problem in radiative transfer. It was observed in parallel that the solution of neuro-fuzzy systems and hybrid systems neuro-fuzzy committee machines, have a poor quality results when using the experimental data with the lowest sensitivity coefficients for the parameters that will be estimated. Moreover, when data are used with greater sensitivity, better results are obtained. This approach seeks to avoid the possibility of non-convergence in such methods.

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Ollé, Tamás. "Klasifikace vzorů pomocí fuzzy neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219728.

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Práce popisuje základy principu funkčnosti neuronů a vytvoření umělých neuronových sítí. Je zde důkladně popsána struktura a funkce neuronů a ukázán nejpoužívanější algoritmus pro učení neuronů. Základy fuzzy logiky, včetně jejich výhod a nevýhod, jsou rovněž prezentovány. Detailněji je popsán algoritmus zpětného šíření chyb a adaptivní neuro-fuzzy inferenční systém. Tyto techniky poskytují efektivní způsoby učení neuronových sítí.

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Března, Filip Samuel. "Využití umělé inteligence jako podpory pro rozhodování v podniku." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2020. http://www.nusl.cz/ntk/nusl-417710.

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Artificial intelligence and fuzzy logic related to it currently belong to very popular and rapidly expanding technological subjects. It finds use in many areas, which also include the process of prediction of future states based on specific finite input characteristics. This master’s thesis deals with predictions that are done in field of agricultural crops growing. Basic principles that are affecting mentioned agricultural growing are explained here, their meaning and significance are specified, these are later on perceived as a key aspect to creation of fuzzy models that are used for prediction. This process is specifically about finding out the most suitable crop on considered parcel for maximization of income. Second part of design section is dedicated to description of approaches for work with fuzzy models and is also used as demonstration of application created for purpose of this thesis.

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Sunny, Mohammed Rabius. "Towards Structural Health Monitoring of Gossamer Structures Using Conductive Polymer Nanocomposite Sensors." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/28797.

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The aim of this research is to calibrate conductive polymer nanocomposite materials for large strain sensing and develop a structural health monitoring algorithm for gossamer structures by using nanocomposites as strain sensors. Any health monitoring system works on the principle of sensing the response (strain, acceleration etc.) of the structure to an external excitation and analyzing the response to find out the location and the extent of the damage in the structure. A sensor network, a mathematical model of the structure, and a damage detection algorithm are necessary components of a structural health monitoring system. In normal operating conditions, a gossamer structure can experience normal strain as high as 50%. But presently available sensors can measure strain up to 10% only, as traditional strain sensor materials do not show low elastic modulus and high electrical conductivity simultaneously. Conductive polymer nanocomposite which can be stretched like rubber (up to 200%) and has high electrical conductivity (sheet resistance 100 Ohm/sq.) can be a possible large strain sensor material. But these materials show hysteresis and relaxation in the variation of electrical properties with mechanical strain. It makes the calibration of these materials difficult. We have carried out experiments on conductive polymer nanocomposite sensors to study the variation of electrical resistance with time dependent strain. Two mathematical models, based on the modified fractional calculus and the Preisach approaches, have been developed to model the variation of electrical resistance with strain in a conductive polymer. After that, a compensator based on a modified Preisach model has been developed. The compensator removes the effect of hysteresis and relaxation from the output (electrical resistance) obtained from the conductive polymer nanocomposite sensor. This helps in calibrating the material for its use in large strain sensing. Efficiency of both the mathematical models and the compensator has been shown by comparison of their results with the experimental data. A prestressed square membrane has been considered as an example structure for structural health monitoring. Finite element analysis using ABAQUS has been carried out to determine the response of the membrane to an uniform transverse dynamic pressure for different damage conditions. A neuro-fuzzy system has been designed to solve the inverse problem of detecting damages in the structure from the strain history sensed at different points of the structure by a sensor that may have a significant hysteresis. Damage feature index vector determined by wavelet analysis of the strain history at different points of the structure are taken by the neuro-fuzzy system as input. The neuro-fuzzy system detects the location and extent of the damage from the damage feature index vector by using some fuzzy rules. Rules associated with the fuzzy system are determined by a neural network training algorithm using a training dataset, containing a set of known input and output (damage feature index vectors, location and extent of damage for different damage conditions). This model is validated by using the sets of input-output other than those which were used to train the neural network. Ph. D.

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Васильєва, Тетяна Анатоліївна, Татьяна Анатольевна Васильева, Tetiana Anatoliivna Vasylieva, and O. Skrynnyk. "Neuro-Genetic Hybrid System for Management of Organizational Development Measures." Thesis, RWTH Aachen University, 2020. https://essuir.sumdu.edu.ua/handle/123456789/85490.

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Current practical experience in measuring the effectiveness of organizational development activities is largely based on the evaluation of surveys. In this paper we present an approach based on an artificial neural network with elements of a fuzzy approach and a genetic algorithm to control organizational development. Based on genetic algorithms, the organizational development measures are initiated, selected, combined or mutated with the goal of finding the best possible solution for each concrete case. Since many variables have the uncertain set of their values, the use of a hybrid neuro-fuzzy mechanism makes it possible to analyze the behavioral components up to the combinations of needs and thereby select the appropriate organizational development measures. The system is designed to ensure the long-term effectiveness of organizational development measures. We supplement the previously known measures of organizational development with technology-based in order to increase the degree of automation in practice. This article is intended as an orientation for other scientists who are researching the same topic and are interested in the current state of the art, as well as for companies who want to ensure compliance with internal company rules using digital tools.

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SILVA, Ana Maria Ribeiro Bastos da. "Avaliação da qualidade da água bruta superficial das barragens de Bita e Utinga de Suape aplicando estatística e sistemas inteligentes." Universidade Federal de Pernambuco, 2015. https://repositorio.ufpe.br/handle/123456789/17404.

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Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-07-15T12:20:57Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Tese SILVA AMRB.pdf: 10197611 bytes, checksum: dfa95dac75e87b0ffef8a344cb8d9996 (MD5) Made available in DSpace on 2016-07-15T12:20:57Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Tese SILVA AMRB.pdf: 10197611 bytes, checksum: dfa95dac75e87b0ffef8a344cb8d9996 (MD5) Previous issue date: 2015-01-30 CNPq Petrobrás A aplicação de técnicas de Análises de Componentes Principais (ACP), Redes Neurais Artificiais (RNA), Lógica Fuzzy e Sistema Neurofuzzy para investigar as alterações da característica da água das barragens de Utinga e do Bita que abastecem de água bruta a ETA Suape é de fundamental importância em função do grande número de variáveis utilizadas para definir a qualidade. Neste trabalho, foram realizadas 10 coletas de água em cada área, no período de novembro de 2007 a agosto de 2012, totalizando 120 amostras. Ainda que o conjunto de dados experimentais obtidos seja reduzido, houve múltiplos esforços em demanda da aquisição de informações da qualidade da água junto aos órgãos oficiais de monitoramento ambiental. Os resultados mostraram uma tendência à degradação da propriedade da água das barragens em decorrência da presença de microrganismos, sais e nutrientes, responsáveis pelo processo de eutrofização, o que se configurou pela maior concentração de fósforo total, Coliformes termotolerantes, e diminuição de pH e OD, provavelmente devido à ocorrência de descarte de efluentes da agroindústria canavieira, industrial e doméstico. A ACP caracterizou mais 76% das amostras permitindo visualizar a existência de mudanças sazonais e uma pequena variação espacial d`água nas barragens. A condição da água das duas barragens foi modelada satisfatoriamente, razoável precisão e confiabilidade com os modelos estatístico e computacionais, para uma quantidade de parâmetros e dados ambientais, que embora limitados foram suficientes para realização deste trabalho. Ainda assim, fica evidente a eficiência e sucesso da utilização do Sistema Neurofuzzy (coeficiente de regressão de 0,608 a 0,925) que combina as vantagens das Redes Neurais e da Lógica Fuzzy em modelar o conjunto de dados da qualidade da água das barragens de Utinga e Bita. The application of techniques such as the Principal Components Analysis (PCAs), Artificial Neural Networks (ANNs), Fuzzy Logic and Neuro-fuzzy Systems for investigating the changes in the water quality characteristics in the Utinga and Bita dams, which supplies raw water to the Suape Wastewater Treatment Plant (WWP), is of great importance due to the high number of variables used to define water quality. In this work were collected 10 water samples used to define water quality, in a period ranging from November 2007 to August 2012, with a total of 120 samples. Although the experimental dataset was limited, there were multiple efforts in gathering information from the Environmental Control Agencies. The results showed a tendency of degradation of the water properties in the dams studied due to the presence of microorganisms, salts and nutrients, responsible for the eutrophication process; result of the higher concentration of total phosphorus, Thermotolerant Coliforms and decrease in pH and DO, probably from the discharge of the sugarcane agroindustry and domestic waste. The PCAs characterised more than 76% of the samples collected, and consequently observing the existence of seasonal changes and small spatial variation of water levels in the dams. The water quality conditions in both dams were satisfactorily modelled, obtaining a reasonable precision and statistical and computational reliability for a certain amount of parameters and environmental data that, even though considered limited, were enough to run this trial. Nonetheless, it becomes evident the efficiency and success in using the Neuro- Fuzzy System (regression coefficient of 0.608 to 0.925), which combines the advantages of both the Neural Networks and Fuzzy Logic in modelling the water quality dataset in the Utinga and Bita dams.

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Aquino, Pedro Luiz da Mota e. "Inteligência computacional aplicada à modelagem e otimização de bioprocessos." Universidade Federal de São Carlos, 2016. https://repositorio.ufscar.br/handle/ufscar/8771.

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Submitted by Aelson Maciera (aelsoncm@terra.com.br) on 2017-05-19T18:15:12Z No. of bitstreams: 1 TesePLMA.pdf: 8101922 bytes, checksum: 456faa861edf6a27b2e7de9a7a271429 (MD5) Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-05-23T20:32:54Z (GMT) No. of bitstreams: 1 TesePLMA.pdf: 8101922 bytes, checksum: 456faa861edf6a27b2e7de9a7a271429 (MD5) Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-05-23T20:33:05Z (GMT) No. of bitstreams: 1 TesePLMA.pdf: 8101922 bytes, checksum: 456faa861edf6a27b2e7de9a7a271429 (MD5) Made available in DSpace on 2017-05-25T14:21:19Z (GMT). No. of bitstreams: 1 TesePLMA.pdf: 8101922 bytes, checksum: 456faa861edf6a27b2e7de9a7a271429 (MD5) Previous issue date: 2016-04-29 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) This work deals with modeling applications, systematic and reliable optimization methodologies of global search, and other computational tools. It is expected that existing computational intelligence methods, encoded in an appropriate tool for the application of process engineering assisted by computer, can lead to useful numerical results for the modeling and optimization of different processes, including biotechnological processes (focus of this work). Thus, different types of methodologies suitable for computer applications, were studied here. The proposed methodologies were implemented and evaluated for the development and optimization of culture media for the fermentation process of Clostridium novyi type B, besides the fermentation process and enzymatic hydrolysis of bagasse associated with the production of bioethanol (1G and 2G). Thus, the potential application of these computational techniques was evaluated to biotechnological systems in different approaches. More specifically, it was performed: Classification of biotechnological systems ( "clustering") in kinetically similar regions to produce cellulosic ethanol (2G ethanol) using fuzzy logic; estimation by global search of kinetic parameters to an alcoholic fermentation model using Simmulated Annealing algorithm (SA) (Contributions to the thematic project FAPESP 2011 / 51902-9); formulation and optimization of economically viable culture media for Clostridium novyi type B using neuro-fuzzy data modeling followed by global search which maximize productivity, also utilizing SA algorithm as a search engine (this step of the project was conducted in partnership with the veterinary pharmaceutical company Vallée SA). The computational tools presented in this work were highly effective for modeling and optimization of the bioprocesses studied. Este trabalho aborda aplicações de modelagem, metodologias sistemáticas e confiáveis de otimização por busca global, além de outras ferramentas computacionais. Espera-se que métodos de inteligência computacional existentes, codificados em uma ferramenta apropriada para a aplicação da engenharia de processos assistida por computador, resultem em resultados numéricos úteis para a modelagem e otimização de diferentes processos, incluindo-se os processos biotecnológicos (foco deste trabalho). Assim, diferentes tipos de metodologias, apropriadas para aplicações em computador, foram aqui estudadas. Os métodos propostos foram aplicados e avaliados ao desenvolvimento e otimização de meios de cultura para o processo fermentativo do microrganismo Clostridium novyi tipo B, além dos processos de fermentação alcoólica e hidrolise enzimática de bagaço de cana, associados à produção de bioetanol (1G e 2G). Desta forma, foi avaliado o potencial de aplicação destas técnicas computacionais aos sistemas biotecnológicos, em diversas abordagens. Mais especificamente, foram realizadas: classificação (“clustering”) de sistemas em regiões cineticamente semelhantes para a produção de etanol celulósico (Etanol 2G) utilizando lógica Fuzzy; estimação por busca global de parâmetros cinéticos do modelo para uma fermentação alcoólica utilizando o algoritmo Simmulated Annealing (SA) (Contribuições ao projeto temático FAPESP 2011/51902-9); formulação e otimização do meio de cultura economicamente viável para o Clostridium novyi tipo B utilizando a modelagem de dados por neuro-fuzzy seguido de busca global da composição de meio que maximize a produtividade utilizando também o algoritmo SA como ferramenta de busca global (esta etapa do projeto foi realizado em parceria com a empresa farmacêutica veterinária Vallée S.A). As ferramentas computacionais apresentadas neste trabalho se mostraram altamente efetivas para a modelagem e otimização dos bioprocessos estudados." FAPESP: 2011/51902-9. FAPESP: 2008/56246-0.

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Arsava, Kemal Sarp. "Modeling, Control and Monitoring of Smart Structures under High Impact Loads." Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-dissertations/105.

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In recent years, response analysis of complex structures under impact loads has attracted a great deal of attention. For example, a collision or an accident that produces impact loads that exceed the design load can cause severe damage on the structural components. Although the AASHTO specification is used for impact-resistant bridge design, it has many limitations. The AASHTO specification does not incorporate complex and uncertain factors. Thus, a well-designed structure that can survive a collision under specific conditions in one region may be severely damaged if it were impacted by a different vessel, or if it were located elsewhere with different in-situ conditions. With these limitations in mind, we propose different solutions that use smart control technology to mitigate impact hazard on structures. However, it is challenging to develop an accurate mathematical model of the integrated structure-smart control systems. The reason is due to the complicated nonlinear behavior of the integrated nonlinear systems and uncertainties of high impact forces. In this context, novel algorithms are developed for identification, control and monitoring of nonlinear responses of smart structures under high impact forces. To evaluate the proposed approaches, a smart aluminum and two smart reinforced concrete beam structures were designed, manufactured, and tested in the High Impact Engineering Laboratory of Civil and Environmental Engineering at WPI. High-speed impact force and structural responses such as strain, deflection and acceleration were measured in the experimental tests. It has been demonstrated from the analytical and experimental study that: 1) the proposed system identification model predicts nonlinear behavior of smart structures under a variety of high impact forces, 2) the developed structural health monitoring algorithm is effective in identifying damage in time-varying nonlinear dynamic systems under ambient excitations, and 3) the proposed controller is effective in mitigating high impact responses of the smart structures.

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Mohammadzadeh, Soroush. "System identification and control of smart structures: PANFIS modeling method and dissipativity analysis of LQR controllers." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-theses/868.

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"Maintaining an efficient and reliable infrastructure requires continuous monitoring and control. In order to accomplish these tasks, algorithms are needed to process large sets of data and for modeling based on these processed data sets. For this reason, computationally efficient and accurate modeling algorithms along with data compression techniques and optimal yet practical control methods are in demand. These tools can help model structures and improve their performance. In this thesis, these two aspects are addressed separately. A principal component analysis based adaptive neuro-fuzzy inference system is proposed for fast and accurate modeling of time-dependent behavior of a structure integrated with a smart damper. Since a smart damper can only dissipate energy from structures, a challenge is to evaluate the dissipativity of optimal control methods for smart dampers to decide if the optimal controller can be realized using the smart damper. Therefore, a generalized deterministic definition for dissipativity is proposed and a commonly used controller, LQR is proved to be dissipative. Examples are provided to illustrate the effectiveness of the proposed modeling algorithm and evaluating the dissipativity of LQR control method. These examples illustrate the effectiveness of the proposed modeling algorithm and dissipativity of LQR controller."

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Shankar, Arunprasath. "ONTOLOGY-DRIVEN SEMI-SUPERVISED MODEL FOR CONCEPTUAL ANALYSIS OF DESIGN SPECIFICATIONS." Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1401706747.

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Kanne, Juliane. "Anwendung von Neuro-Fuzzy Methoden für die Robotersteuerung." [S.l. : s.n.], 2004. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB11312760.

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Burton, Anthony Richard. "A hybrid neuro-genetic pattern evolution system applied to musical composition." Thesis, University of Surrey, 1998. http://epubs.surrey.ac.uk/2875/.

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Hafezi, Nazila. "An integrated software package for model-based neuro-fuzzy classification of small airway dysfunction." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2009. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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39

Aghakhani, Sara. "Neuro-fuzzy architecture based on complex fuzzy logic." 2010. http://hdl.handle.net/10048/891.

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Thesis (M.Sc.)--University of Alberta, 2010. Title from PDF file main screen (viewed on May 7, 2010). A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science in Software Engineering and Intelligent Systems, Department of Electrical and Computer Engineering, University of Alberta. Includes bibliographical references.

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Sara, Aghakhani. "Neuro-fuzzy architectures based on complex fuzzy logic." Master's thesis, 2010. http://hdl.handle.net/10048/891.

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Complex fuzzy logic is a new type of multi-valued logic, in which truth values are drawn from the unit disc of the complex plane; it is thus a generalization of the familiar infinite-valued fuzzy logic. At the present time, all published research on complex fuzzy logic is theoretical in nature, with no practical applications demonstrated. The utility of complex fuzzy logic is thus still very debatable. In this thesis, the performance of ANCFIS is evaluated. ANCFIS is the first machine learning architecture to fully implement the ideas of complex fuzzy logic, and was designed to solve the important machine-learning problem of time-series forecasting. We then explore extensions to the ANCFIS architecture. The basic ANCFIS system uses batch (offline) learning, and was restricted to univariate time series prediction. We have developed both an online version of the univariate ANCFIS system, and a multivariate extension to the batch ANCFIS system. Software Engineering and Intelligent Systems

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Multani, Munish. "Design and Optimization of Intelligent PI Controllers (Fuzzy and Neuro-Fuzzy) for HVDC Transmission System." Thesis, 2010. http://hdl.handle.net/10155/103.

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This thesis deals with enhancing the performance of Fuzzy Logic (FL) based PI controllers for High Voltage Direct Current Transmission Systems (HVDC) by optimizing the key parameters i.e. membership functions (MFs) and fuzzy rule base in the controllers design. In the first part of the thesis, an adaptive Fuzzy PI controller is designed and the effect of various MF shapes, widths and distribution on the performance of a FL controlled HVDC system under different system conditions is studied with the aim of selecting a MF which minimizes the total control error. Simulated results show that the shape, width and distribution of a MF influences the performance of the FL controller and concludes that nonlinear MFs (i.e. Gaussian) offer a more better choice than linear (i.e. Triangular) MFs as the former provides a smoother transition at the switching points and thus propose a better controller. In the second part of the thesis, a Neuro-Fuzzy (NF) controller to update the fuzzy rule base with changing system conditions is proposed, which in turn adjusts the PI gains of a conventional PI controller. Results from simulations illustrate the potential of the proposed control scheme as the NF controller successfully adapts to different system conditions and is able to minimize the total current error. UOIT

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Patel, Pretesh Bhoola. "A forecasting of indices and corresponding investment decision making application." Thesis, 2007. http://hdl.handle.net/10539/2191.

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Student Number : 9702018F - MSc(Eng) Dissertation - School of Electrical and Information Engineering - Faculty of Engineering and the Built Environment Due to the volatile nature of the world economies, investing is crucial in ensuring an individual is prepared for future financial necessities. This research proposes an application, which employs computational intelligent methods that could assist investors in making financial decisions. This system consists of 2 components. The Forecasting Component (FC) is employed to predict the closing index price performance. Based on these predictions, the Stock Quantity Selection Component (SQSC) recommends the investor to purchase stocks, hold the current investment position or sell stocks in possession. The development of the FC module involved the creation of Multi-Layer Perceptron (MLP) as well as Radial Basis Function (RBF) neural network classifiers. TCategorizes that these networks classify are based on a profitable trading strategy that outperforms the long-term “Buy and hold” trading strategy. The Dow Jones Industrial Average, Johannesburg Stock Exchange (JSE) All Share, Nasdaq 100 and the Nikkei 225 Stock Average indices are considered. TIt has been determined that the MLP neural network architecture is particularly suited in the prediction of closing index price performance. Accuracies of 72%, 68%, 69% and 64% were obtained for the prediction of closing price performance of the Dow Jones Industrial Average, JSE All Share, Nasdaq 100 and Nikkei 225 Stock Average indices, respectively. TThree designs of the Stock Quantity Selection Component were implemented and compared in terms of their complexity as well as scalability. TComplexity is defined as the number of classifiers employed by the design. Scalability is defined as the ability of the design to accommodate the classification of additional investment recommendations. TDesigns that utilized 1, 4 and 16 classifiers, respectively, were developed. These designs were implemented using MLP neural networks, RBF neural networks, Fuzzy Inference Systems as well as Adaptive Neuro-Fuzzy Inference Systems. The design that employed 4 classifiers achieved low complexity and high scalability. As a result, this design is most appropriate for the application of concern. It has also been determined that the neural network architecture as well as the Fuzzy Inference System implementation of this design performed equally well.

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Nguyen, Huy Huynh. "A neural fuzzy approach to modeling the thermal behavior of power transformers." Thesis, 2007. https://vuir.vu.edu.au/1495/.

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This thesis presents an investigation and a comparative study of four different approaches namely ANSI/IEEE standard models, Adaptive Neuro-Fuzzy Inference System (ANFIS), Multilayer Feedforward Neural Network (MFNN) and Elman Recurrent Neural Network (ERNN) to modeling and prediction of the top and bottom-oil temperatures for the 8 MVA Oil Air (OA)-cooled and 27 MVA Forced Air (FA)-cooled class of power transformers. The models were derived from real data of temperature measurements obtained from two industrial power installations. A comparison of the proposed techniques is presented for predicting top and bottom-oil temperatures based on the historical data measured over a 35 day period for the first transformer and 4.5 days for the second transformer with either a half or a quarter hour sampling time. Comparisons of the results obtained indicate that the hybrid neuro-fuzzy network is the best candidate for the analysis and prediction of the power transformer top and bottom-oil temperatures. The ANFIS demonstrated the best comparative performance in temperature prediction in terms of Root Mean Square Error (RMSE) and peak error.

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Paiva, Rui Pedro Pinto de Carvalho e. "Identificação neuro-difusa: aspectos de interpretabilidade." Master's thesis, 1999. http://hdl.handle.net/10316/15368.

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Dissertação de mestrado apresentada à Faculdade de Ciências e Tecnologia da Universidade de Coimbra No momento presente da História, é lícito afirmar-se que a humanidade se encontra em plena era da informação. De facto, em qualquer aspecto da sociedade, desde as actividades de lazer até aos mais complexos sistemas de produção, é notória a presença e influência das tecnologias de informação. Assim, assiste-se presentemente a um forte impulso na investigação, desenvolvimento e aplicação de metodologias de computação aos processos industriais de produção. Na verdade, o elevado grau de complexidade que os caracteriza, acompanhado de uma necessidade crescente de desempenho como forma de dar resposta às leis de mercado, exige a utilização de estratégias cada vez mais sofisticadas. Uma das áreas que tem merecido uma atenção particular tem sido a soft computing, a qual engloba metodologias tais como a lógica difusa, redes neuronais e algoritmos genéticos, de forma simples ou combinada, constituindo um dos pilares dos sistemas de informação inteligentes. Neste contexto, a dissertação apresentada pretende contribuir para a compreensão do potencial associado às técnicas neuro-difusas como mecanismo de identificação de sistemas. Assim, numa primeira fase introdutória, são apresentados e discutidos os princípios básicos da lógica difusa, sistemas difusos e redes neuronais, enquadrados na temática da identificação de sistemas. Neste trabalho, são estudadas diversas estruturas difusas, nomeadamente os sistemas de Takagi-Sugeno de ordem 0 e 1, bem como sistemas linguísticos. Neste sentido, são abordados dois aspectos essenciais da identificação difusa: a aprendizagem da estrutura e a aprendizagem de parâmetros. No primeiro ponto é prestada especial atenção à utilização de técnicas de agrupamento de classes, destacando-se, de entre estas, o algoritmo de agrupamento subtractivo. Ainda em relação à aprendizagem da estrutura, é abordada a questão da selecção de entradas relevantes. Relativamente à aprendizagem de parâmetros, a mesma é conduzida com recurso ao treino de uma rede neuronal difusa pelo algoritmo de retropropagação do erro, sendo, em algumas situações, utilizados esquemas híbridos baseados em optimização linear e não linear. Ainda em relação a este ponto, o problema da aprendizagem incremental de parâmetros é abordado, ainda que de forma superficial. Um aspecto relevante no contexto da implementação de modelos difusos prende-se com a exploração do potencial que lhes é inerente em termos de transparência do modelo final. Assim sendo, são apresentados alguns estudos originais em termos de estratégias que visem a manutenção da interpretabilidade dos modelos durante a aprendizagem de parâmetros, as quais se baseiam em medidas de similaridade e na aprendizagem restringida de parâmetros. As metodologias referidas foram aplicadas a alguns casos de estudo, e.g., a série caótica Mackey-Glass e a fornalha de Gás Box-Jenkins, os quais confirmaram as suas capacidades de modelização, assim com a adequação das técnicas difusas na implementação de modelos interpretáveis. As mesmas técnicas foram aplicadas a um sistema industrial, nomeadamente uma planta de branqueamento de pasta da papel. Contudo, os resultados obtidos não foram totalmente satisfatórios, em virtude da deficiente qualidade dos dados de identificação. Na realização do estudo efectuado, os algoritmos descritos neste trabalho foram implementados na linguagem de programação C++, preparando-se neste momento a sua integração numa interface gráfica por forma a que a ferramenta computacional desenvolvida possa constituir um auxílio no estudo dos problemas analisados nesta dissertação, tanto com funções didácticas como de investigação científica. Nowadays, humankind is in the era of information. In fact, in most of the aspects of today’s society, from leisure activities to complex production systems, the presence and influence of the technologies of information is clear. Thus, there is presently a strong impulse towards the research, development and application of computing methodologies in industrial production systems. Actually, the high degree of complexity that characterizes those systems, as well as the increasing necessities in terms of performance in order to cope with the rules of the market, demands strategies more and more sophisticated. One of the areas that has deserved a particular attention is soft computing, which includes techniques like fuzzy logic, neural networks and genetic algorithms, in a simple or combined fashion, and constitutes itself as the basis for intelligent information systems. In this context, the study carried out aims to contribute to the comprehension of the potential associated to neuro-fuzzy techniques as a mechanism for system identification. In a first introductory phase, the grounds of fuzzy logic, fuzzy systems and neural networks are presented and discussed, integrated in the problem of system identification. In this work, several fuzzy structures are analyzed, namely Takagi-Sugeno (zero and first order) systems and linguistic systems. Two major concerns of fuzzy identification are studied: structure learning and parameter learning. Referring to the first item, clustering techniques receive a deeper attention, especially subtractive clustering. Still in the same point, the questions related to relevant input selection are addressed. As for parameter learning, this task is carried out after the determination of a structure, based on the training of a fuzzy neural network via error backpropagation. In some situations, hybrid learning schemes are also utilized, which result from the combination of both linear and nonlinear optimization algorithms. In the point of parameter learning, the problem of online learning is also addressed, though superficially. A relevant matter in the context of fuzzy identification relates to the use of their potential in terms of model transparency. In this way, some original studies are performed, regarding the construction of interpretable fuzzy models, which are based on similarity measures and restricted parameter learning. The subjects mentioned above were applied to same case studies, e.g., the Mackey-Glass chaotic time series and the Box-Jenkins gas furnace, which confirmed their modeling capabilities, as well as the adequacy of fuzzy techniques for the building up of interpretable models. The same techniques were applied to an industrial plant, namely a pulp bleaching plant. However, the results obtained so far are not totally satisfactory, due to bad data quality, which resulted from a deficient sampling time, as well as insufficient excitation of some input variables. The techniques studied are implemented in software, and constitute the core of an application, which is being developed to assist the comprehension and analysis of the main issues regarding fuzzy identification. The resulting software tool will be used both with research and pedagogical goals.

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Dissertations / Theses on the topic 'Neuro-fuzzy logic'

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