Relevant bibliographies by topics / Neuro-fuzzy logic

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  2. Dissertations / Theses
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Academic literature on the topic 'Neuro-fuzzy logic'

Author: Grafiati
Published: 2 June 2025
Last updated: 22 June 2025

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Journal articles on the topic "Neuro-fuzzy logic"

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Thakur, Amey. "Neuro-Fuzzy: Artificial Neural Networks & Fuzzy Logic." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 128–35. http://dx.doi.org/10.22214/ijraset.2021.37930.

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Abstract: Neuro Fuzzy is a hybrid system that combines Artificial Neural Networks with Fuzzy Logic. Provides a great deal of freedom when it comes to thinking. This phrase, on the other hand, is frequently used to describe a system that combines both approaches. There are two basic streams of neural network and fuzzy system study. Modelling several elements of the human brain (structure, reasoning, learning, perception, and so on) as well as artificial systems and data: pattern clustering and recognition, function approximation, system parameter estimate, and so on. In general, neural networks and fuzzy logic systems are parameterized nonlinear computing methods for numerical data processing (signals, images, stimuli). These algorithms can be integrated into dedicated hardware or implemented on a general-purpose computer. The network system acquires knowledge through a learning process. Internal parameters are used to store the learned information (weights). Keywords: Artificial Neural Networks (ANNs), Neural Networks (NNs), Fuzzy Logic (FL), Neuro-Fuzzy, Probability Reasoning, Soft Computing, Fuzzification, Defuzzification, Fuzzy Inference Systems, Membership Function.
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Titov, Andrei P. "SOFTWARE IMPLEMENTATION OF THE CO-ACTIVE NEURO-FUZZY INFERENCE SYSTEM." RSUH/RGGU Bulletin. Series Information Science. Information Security. Mathematics, no. 2 (2024): 26–43. http://dx.doi.org/10.28995/2686-679x-2024-2-26-43.

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The article deals with the implementation of a neural network with fuzzy logic based on the Co-Active Neuro-Fuzzy Inference System (CANFIS) model. The CANFIS model is an adaptive neuro-fuzzy system that combines neural networks and fuzzy logic for processing data with uncertainty and fuzziness. CANFIS uses fuzzy rules and output mechanisms to convert input data into output values. It consists of several layers, including an input layer, hidden layers and an output layer, where each layer contains neurons performing fuzzy activation and output of results. The relevance of the work lies in the fact that the software implementation of the CANFIS model, based on the STL of the C++ language, is of great importance in the field of machine learning, artificial intelligence and data analysis. The work’s results can be applied in various fields, including when making decisions based on fuzzy logic. Special feature of the studied and developed model is to create an adaptive model capable of modeling systems with uncertainty and blurriness. The developed model is able to process data and make decisions based on fuzzy rules. CANFIS finds applications in various fields, including forecasting, management, classification and data analysis. It can be concluded that the developed neural network with fuzzy logic can be effectively applied in various fields where time series forecasting, system management and decision-making based on fuzzy information are used.
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Katayama, Ryu. "Applications of Neuro Fuzzy Technology in Consumer Electronics Products." Journal of Robotics and Mechatronics 7, no. 1 (1995): 2–8. http://dx.doi.org/10.20965/jrm.1995.p0002.

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In recent years, intelligent industrial systems and consumer electronic products have been widely and intensively developed. Fuzzy logic, neural network, and neuro fuzzy technology, which integrates both approaches, are now regarded as an effective method to realize such intelligent features. In this paper, a review of the fuzzy boom in the consumer electronics market of Japan is presented. Typical applications of home appliances using fuzzy logic and neuro fuzzy technology are then described. Finally, methods and tools for developing fuzzy systems such as self-tuning and fuzzy modeling are reviewed.
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Chandrasekhar, Tadi, and Ch Sumanth Kumar. "Improved Facial Identification Using Adaptive Neuro-Fuzzy Logic Inference System." Indian Journal Of Science And Technology 16, no. 13 (2023): 1014–20. http://dx.doi.org/10.17485/ijst/v16i13.1833.

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Titov, Andrei P. "ANALYSIS OF MODELS OF ADAPTIVE NEURO-FUZZY SYSTEMS." RSUH/RGGU Bulletin. Series Information Science. Information Security. Mathematics, no. 1 (2024): 21–35. http://dx.doi.org/10.28995/2686-679x-2024-1-21-35.

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The article deals with the study of basic methods for models of adaptive neuro-fuzzy systems. Based on the analysis, the strengths of neural networks and fuzzy logic were found, that became powerful tools for solving complex modeling and forecasting issues. There is studying and analyzing the adaptive neural network, which is a class of neural networks that have the ability to change their structure and parameters in the process of learning and adaptation to new data and conditions and besides the article studies the Gaussian membership function, also known as the normal membership function or the Gauss-type membership function, which is a valuable tool in the f ield of fuzzy logic and fuzzy systems. The paper provides as well an analysis of the generalized Bell membership function, also known as the Bell type membership function or Bell function, which plays an important role in the field of fuzzy logic and fuzzy systems. Furthermore it analyzes the Tsukamoto model, which is one of the main models of fuzzy logic. The author opted to choose the Co-Active Neuro-Fuzzy Inference System model, which is an adaptive neuro-fuzzy system that combines neural networks and fuzzy logic for processing data with uncertainty and fuzziness. With the further implementation of the combined model based on the abovelisted models based on the STL of the C++ language, thus the neural network model is obtained, the model with versatility, that is achieved by using a combination of those models. That will facilitate its easy modification and adaptation to various tasks.
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Jindal, Nikita, Jimmy Singla, Balwinder Kaur, et al. "Fuzzy Logic Systems for Diagnosis of Renal Cancer." Applied Sciences 10, no. 10 (2020): 3464. http://dx.doi.org/10.3390/app10103464.

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Renal cancer is a serious and common type of cancer affecting old ages. The growth of such type of cancer can be stopped by detecting it before it reaches advanced or end-stage. Hence, renal cancer must be identified and diagnosed in the initial stages. In this research paper, an intelligent medical diagnostic system to diagnose renal cancer is developed by using fuzzy and neuro-fuzzy techniques. Essentially, for a fuzzy inference system, two layers are used. The first layer gives the output about whether the patient is having renal cancer or not. Similarly, the second layer detects the current stage of suffering patients. While in the development of a medical diagnostic system by using a neuro-fuzzy technique, the Gaussian membership functions are used for all the input variables considered for the diagnosis. In this paper, the comparison between the performance of developed systems has been done by taking some suitable parameters. The results obtained from this comparison study show that the intelligent medical system developed by using a neuro-fuzzy model gives the more precise and accurate results than existing systems.
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Ashigwuike, Evans Chinemezu, and Stephen Adole Benson. "Optimal Location and Sizing of Distributed Generation in Distribution Network Using Adaptive Neuro-Fuzzy Logic Technique." European Journal of Engineering Research and Science 4, no. 4 (2019): 83–89. http://dx.doi.org/10.24018/ejers.2019.4.4.1237.

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The growing gap between electric power generated and that demanded is of utmost concern especially in developing economy, hence calling for measures to argument the existing power generated of which DG is a more viable aspect to explore in curtailing this challenges; although been confronted with issue of location and sizing. This research applied Adaptive neuro fuzzy logic technique to optimize DG location and size. A 24 bus radial network was used to demonstrate this process and having a suitable location and size at optimal position reduces power losses and also improves the voltage profile at the buses. The method was simulated using ANFIS toolbox MATLAB R2013b (8.2.0.701) 64-bit software and tested using Gwagwalada injection sub-station feeder 1 system. The results obtained were compared to that obtained using ANN. It was observed that adaptive neuro fuzzy logic technique performed better in terms of reducing power losses compared to ANN technique. The percentage reduction in the power loss at the buses cumulatively is 48.96% for ANN while adaptive neuro fuzzy logic technique is 49.21%. The voltage profile of the networks after optimizing the DG location and sizes using adaptive neuro fuzzy logic technique were also found to be much improved with the lowest bus voltage improved from 0.9284 to 1.05pu.
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Ashigwuike, Evans Chinemezu, and Stephen Adole Benson. "Optimal Location and Sizing of Distributed Generation in Distribution Network Using Adaptive Neuro-Fuzzy Logic Technique." European Journal of Engineering and Technology Research 4, no. 4 (2019): 83–89. http://dx.doi.org/10.24018/ejeng.2019.4.4.1237.

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The growing gap between electric power generated and that demanded is of utmost concern especially in developing economy, hence calling for measures to argument the existing power generated of which DG is a more viable aspect to explore in curtailing this challenges; although been confronted with issue of location and sizing. This research applied Adaptive neuro fuzzy logic technique to optimize DG location and size. A 24 bus radial network was used to demonstrate this process and having a suitable location and size at optimal position reduces power losses and also improves the voltage profile at the buses. The method was simulated using ANFIS toolbox MATLAB R2013b (8.2.0.701) 64-bit software and tested using Gwagwalada injection sub-station feeder 1 system. The results obtained were compared to that obtained using ANN. It was observed that adaptive neuro fuzzy logic technique performed better in terms of reducing power losses compared to ANN technique. The percentage reduction in the power loss at the buses cumulatively is 48.96% for ANN while adaptive neuro fuzzy logic technique is 49.21%. The voltage profile of the networks after optimizing the DG location and sizes using adaptive neuro fuzzy logic technique were also found to be much improved with the lowest bus voltage improved from 0.9284 to 1.05pu.
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Biswas, Saroj, Monali Bordoloi, and Biswajit Purkayastha. "Review on Feature Selection and Classification using Neuro-Fuzzy Approaches." International Journal of Applied Evolutionary Computation 7, no. 4 (2016): 28–44. http://dx.doi.org/10.4018/ijaec.2016100102.

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This research article attempts to provide a recent survey on neuro-fuzzy approaches for feature selection and classification. Feature selection acts as a catalyst in reducing computation time and dimensionality, enhancing prediction performance or accuracy and curtailing irrelevant or redundant data. The neuro-fuzzy approach is used for feature selection and for providing some insight to the user about the symbolic knowledge embedded within the network. The neuro–fuzzy approach combines the merits of neural network and fuzzy logic to solve many complex machine learning problems. The objective of this article is to provide a generic introduction and a recent survey to neuro-fuzzy approaches for feature selection and classification in a wide area of machine learning problems. Some of the existing neuro-fuzzy models are also applied on standard datasets to demonstrate the applicability of neuro-fuzzy approaches.
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Болгов, А. А. "RISK ASSESSMENT USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM." ИНФОРМАЦИЯ И БЕЗОПАСНОСТЬ, no. 4(-) (December 23, 2022): 521–30. http://dx.doi.org/10.36622/vstu.2022.25.4.006.

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В работе предлагается использование адаптивной нейро-нечеткой системы вывода для оценки риска. Проводится подробный обзор адаптивной нейро-нечеткой системы вывода, выделяя основные свойства этой системы в области методов оценки рисков. Приведены основные преимущества использования адаптивной нейро-нечеткой системы вывода. Рассматривается архитектура адаптивной нейро-нечеткой системы вывода. Выделены и рассмотрены основные методы обучения системы. Предложены методы оценки эффективности модели на основе адаптивной нейро-нечеткой системы вывода для оценки риска. Представлен алгоритм внедрения адаптивной нейро-нечеткой системы вывода. Проводятся эксперименты, которые показывают влияние процесса обучения на форму функций принадлежности системы нечеткой логики. Выполнено сравнение результатов оценки риска, полученных с помощью нечеткой логики и при использовании адаптивной нейро-нечеткой системы выводы. The work proposes the use of an adaptive neuro-fuzzy inference system for risk assessment. A detailed review of the adaptive neuro-fuzzy inference system is carried out, highlighting the main properties of this system in the field of risk assessment methods. The main advantages of using an adaptive neuro-fuzzy inference system are given. The architecture of an adaptive neuro-fuzzy inference system is considered. The main methods of teaching the system are highlighted and considered. Methods for evaluating the effectiveness of the model based on an adaptive neuro-fuzzy inference system for risk assessment are proposed. An algorithm for implementing an adaptive neuro-fuzzy inference system is presented. Experiments are being conducted that show the influence of the learning process on the form of the membership functions of the fuzzy logic system. The results of risk assessment obtained using fuzzy logic and using adaptive neuro-fuzzy inference system are compared.
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Dissertations / Theses on the topic "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)<br>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 ...<br>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<br>Banca: Evaldo Assunção<br>Banca: Rodrigo Cardim<br>Banca: Cristiano Quevedo Andrea<br>Banca: Ruy de Oliveira<br>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 ...<br>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 ...<br>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<br>ELETROBRAS - CENTRAIS ELÉTRICAS BRASILEIRAS S. A.<br>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<br>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)<br>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 ...<br>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<br>Banca: Edvaldo Assunção<br>Banca: Aparecido Augusto de Carvalho<br>Banca: Cristiano Quevedo Andrea<br>Banca: Valtemir Emerencio do Nascimento<br>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 ...<br>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<br>Doutor
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Books on the topic "Neuro-fuzzy logic"

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Horia-Nicolai, Teodorescu, Kandel Abraham, and Jain L. C, eds. Fuzzy and neuro-fuzzy systems in medicine. CRC Press, 1999.

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Roy, Samir. Introduction to soft computing: Neuro-fuzzy and genetic algorithms. Dorling Kindersley (India), 2013.

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Lee, R. S. T. Fuzzy-neuro approach to agent applications: From the AI perspective to modern ontology. Springer, 2006.

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Grima, Mario Alvarez. Neuro-fuzzy modeling in engineering geology: Applications to mechanical rock excavation, rock strength estimation, and geological mapping. A.A. Balkema, 2000.

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Fuzzy Logic and Neuro Fuzzy Applications Explained (Bk/Disk). Prentice Hall, 1995.

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Lee, Raymond S. T. Fuzzy-Neuro Approach to Agent Applications. Springer, 2005.

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Fuzzy and Neuro-Fuzzy Systems in Medicine. Taylor & Francis Group, 2017.

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Bothe, Hans-Heinrich. Neuro-Fuzzy-Methoden: Einführung in Theorie und Anwendungen. Springer, 1996.

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Ivancevic, Tijana T., and Vladimir G. G. Ivancevic. Neuro-Fuzzy Associative Machinery for Comprehensive Brain and Cognition Modelling. Springer, 2010.

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Ivancevic, Vladimir G., and Tijana T. Ivancevic. Neuro-Fuzzy Associative Machinery for Comprehensive Brain and Cognition Modelling. Springer London, Limited, 2007.

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Book chapters on the topic "Neuro-fuzzy logic"

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Brahim, Kais. "Neuro-Fuzzy Inferenz-Systeme." In Fuzzy Logic. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-78694-5_18.

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Schoder, Dagmar, and Hans Nücke. "Neuronale Netze und Fuzzy Logic in der Automatisierungstechnik." In Neuro + Fuzzy. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-95754-3_3.

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Teodorescu, H. N. L., and T. Yamakawa. "Neuro-fuzzy Systems: Hybrid Configurations." In Fuzzy Logic. Vieweg+Teubner Verlag, 1996. http://dx.doi.org/10.1007/978-3-322-88955-3_9.

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Bothe, Hans-Heinrich. "Einführung in die Fuzzy Logic." In Neuro-Fuzzy-Methoden. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-58859-4_2.

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Reznik, Leonid. "Neuro-Fuzzy Control Applications: Looking for New Areas and Techniques?" In Fuzzy Logic. Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1806-2_25.

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Maghooli, K., and A. M. Eftekhari Moghadam. "Development of Neuro-fuzzy System for Image Mining." In Fuzzy Logic and Applications. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11676935_4.

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Bede, Barnabas. "Artificial Neural Networks and Neuro-Fuzzy Systems." In Mathematics of Fuzzy Sets and Fuzzy Logic. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35221-8_14.

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Castellano, Giovanna, Anna Maria Fanelli, and Maria Alessandra Torsello. "A System for Deriving a Neuro-Fuzzy Recommendation Model." In Fuzzy Logic and Applications. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02282-1_35.

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Azizan, Farah Liyana, Saratha Sathasivam, Majid Khan Majahar Ali, and Shehab Abdulhabib Saeed Alzaeemi. "Solving HornSAT Fuzzy Logic Neuro-symbolic Integration." In Studies in Systems, Decision and Control. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04028-3_5.

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Nagarajan, D., Khusbhu Chourashia, and A. Udhayakumar. "Neuro-Fuzzy Logic Application in Speech Recognition." In Advances in Intelligent Systems and Computing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3611-3_1.

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Conference papers on the topic "Neuro-fuzzy logic"

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Körösi, Ladislav, Jana Paulusová, and Oliver Halaš. "Adaptive Neuro Fuzzy Inference System for Programmable Logic Controller." In 2025 Cybernetics & Informatics (K&I). IEEE, 2025. https://doi.org/10.1109/ki64036.2025.10916472.

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Amin, Ahmad Faishol, Ronny Cahyadi Utomo, and Khoirul Azis Rifa’i. "Comparing Fuzzy Logic Controller (FLC) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for Auto-Cooling System in Generator Rotor Straightening." In 2024 4th International Conference of Science and Information Technology in Smart Administration (ICSINTESA). IEEE, 2024. http://dx.doi.org/10.1109/icsintesa62455.2024.10747980.

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Kandukuri, Saritha, D. Ravi Kishore, Sunnapu Bhanu Prakash, Penna Ashok, and Koruprolu Jayadeep. "Neuro Fuzzy Logic Algorithm Based WECS-PMSG Interfaced Water pumping System Along with Battery-Energy Management System." In 2024 International Conference on Recent Innovation in Smart and Sustainable Technology (ICRISST). IEEE, 2024. https://doi.org/10.1109/icrisst59181.2024.10921814.

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B, Kavya Santhoshi, Ravi Kishore D, Nivesh Arja, V. V. Satya Sai Naga Geethika Baladari, and Chaitanya Kumar Guttula. "High Gain KY Converter For Grid Tied Clean Energy PV System Using Cascaded Neuro Fuzzy Logic MPPT Algorithm." In 2024 International Conference on Recent Innovation in Smart and Sustainable Technology (ICRISST). IEEE, 2024. https://doi.org/10.1109/icrisst59181.2024.10921792.

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Srichiangsa, Theeraphong, Piyapath Siratarnsophon, Sirichai Wattanasophon, and Sarinee Ouitrakul. "Comparative Analysis of PID, Self-Tunning PID, and Adaptive Neuro-Fuzzy Logic Inference System Controllers for BLDC Motor Speed Control." In 2024 27th International Conference on Electrical Machines and Systems (ICEMS). IEEE, 2024. https://doi.org/10.23919/icems60997.2024.10921276.

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Tayal, Shikha, Taskeen Zaidi, and Preeti Gera. "Utilization of Support Vector Machines (SVM), Fuzzy Logic (FL) & Adaptive Neuro-Fuzzy Inference System (ANFIS) for Carrying Out Proficient Energy Routing in 5G Wireless Networks." In 2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI). IEEE, 2024. https://doi.org/10.1109/icscai61790.2024.10866528.

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Allur, Naga Sushma, Koteswararao Dondapati, Himabindu Chetlapalli, Sharadha Kodadi, Durga Praveen Deevi, and Purandhar N. "Robotic Automation Dynamic Hybrid Neuro-Fuzzy and Deep Learning Framework with GRU-BiLSTM, Capsule Networks, Type-2 Fuzzy Logic and CNN-TCN for Accurate IoMT-Based Chronic Kidney Disease Detection." In 2025 International Conference on Computer, Electrical & Communication Engineering (ICCECE). IEEE, 2025. https://doi.org/10.1109/iccece61355.2025.10940081.

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Misra, R. B. "Tamper detection using neuro-fuzzy logic." In Ninth International Conference on Metering and Tariffs for Energy Supply. IEE, 1999. http://dx.doi.org/10.1049/cp:19990115.

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Ruprecht, Blake, Wenlong Wu, Muhammad Aminul Islam, et al. "Possibilistic Clustering Enabled Neuro Fuzzy Logic." In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2020. http://dx.doi.org/10.1109/fuzz48607.2020.9177593.

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Ali Adem, Mohammed. "Energy Optimization of Wireless Sensor Network Using Neuro-Fuzzy Algorithms." In LatinX in AI at Neural Information Processing Systems Conference 2019. Journal of LatinX in AI Research, 2019. http://dx.doi.org/10.52591/lxai2019120814.

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Wireless sensor network (WSN) is one of the recent technologies in communication and engineering world to assist various civilian and military applications. They are deployed remotely in sever environment which doesn’t have an infrastructure. Energy is a limited resource that needs efficient management to work without any failure. Energy efficient clustering of WSN is the ultimate mechanism to conserve energy for longtime. The major objective of this research is to efficiently consume energy based on the Neuro-Fuzzy approach particularly adaptive Neuro fuzzy inference system (ANFIS). The significance of this study is to examine the challenges of energy efficient algorithms and the network lifetime on WSN so that they can assist several applications. Clustering is one of the hierarchical based routing protocols, which manage the communication between sensor nodes and sink via Cluster Head (CH), CH is responsible to send and receive information from multiple sensor nodes and multiple base stations (BS). There are various algorithms that can efficiently select appropriate CH and localize the membership of cluster with fuzzy logic classification parameters to minimize periodic clustering which consumes more energy and we have applied neural network learning algorithm to learn various patterns based on the fuzzy rules and measured how much energy has saved from random clustering. Finally, we have compared to our Neuro-Fuzzy logic and consequently demonstrated that our Neuro-Fuzzy model outperforms than random model.
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Reports on the topic "Neuro-fuzzy logic"

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Lavrentieva, Olena O., Ihor O. Arkhypov, Olexander I. Kuchma, and Aleksandr D. Uchitel. Use of simulators together with virtual and augmented reality in the system of welders’ vocational training: past, present, and future. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3748.

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The article discusses the theory and methods of simulation training, its significance in the context of training specialists for areas where the lack of primary qualification is critical. The most widespread hardware and software solutions for the organization welders' simulation training that use VR- and AR- technologies have been analyzed. A review of the technological infrastructure and software tools for the virtual teaching-and-production laboratory of electric welding has been made on the example of the achievements of Fronius, MIMBUS, Seabery. The features of creating a virtual simulation of the welding process using modern equipment based on studies of the behavioral reactions of the welder have been shown. It is found the simulators allow not only training, but also one can build neuro-fuzzy logic and design automated and robotized welding systems. The functioning peculiarities of welding's simulators with AR have been revealed. It is shown they make it possible to ensure the forming basic qualities of a future specialist, such as concentration, accuracy and agility. The psychological and technical aspects of the coaching programs for the training and retraining of qualified welders have been illustrated. The conclusions about the significant advantages of VR- and AR-technologies in comparison with traditional ones have been made. Possible directions of the development of simulation training for welders have been revealed. Among them the AR-technologies have been presented as such that gaining wide popularity as allow to realize the idea of mass training in basic professional skills.
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