To see the other types of publications on this topic, follow the link: Fuzzy logic inference system.
Dissertations / Theses on the topic 'Fuzzy logic inference system'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Fuzzy logic inference system.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
Rybalka, A. I., A. S. Kutsenko, and S. V. Kovalenko. "Modelling of an automated food quality assessment system based on fuzzy inference." Thesis, Харківський національний університет радіоелектроніки, 2020. http://openarchive.nure.ua/handle/document/14769.
Full textAbstract:
The purpose of this study is to create a methodology for developing an automated system for assessing the quality of food products based on a comprehensive quality indicator and the use of fuzzy logic theory, namely, fuzzy inference. In our opinion, such an approach to quality assessment can reduce the subjective component that has a significant impact on making a final decision.
The system, built on a given algorithm, allows us to assess the quality of food products, taking into account the data of laboratory studies on measurable quality indicators and expert opinions on difficult to measure indicators.
2
Funsten, Brad Thomas Mr. "ECG Classification with an Adaptive Neuro-Fuzzy Inference System." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1380.
Full textAbstract:
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.
3
GarcÃa, Z. Yohn E. "Fuzzy logic in process control: A new fuzzy logic controller and an improved fuzzy-internal model controller." Scholar Commons, 2006. http://scholarcommons.usf.edu/etd/2529.
Full textAbstract:
Two fuzzy controllers are presented. A fuzzy controller with intermediate variable designed for cascade control purposes is presented as the FCIV controller. An intermediate variable and a new set of fuzzy logic rules are added to a conventional Fuzzy Logic Controller (FLC) to build the Fuzzy Controller with Intermediate Variable (FCIV). The new controller was tested in the control of a nonlinear chemical process, and its performance was compared to several other controllers. The FCIV shows the best control performance regarding stability and robustness. The new controller also has an acceptable performance when noise is added to the sensor signal. An optimization program has been used to determine the optimum tuning parameters for all controllers to control a chemical process. This program allows obtaining the tuning parameters for a minimum IAE (Integral absolute of the error). The second controller presented uses fuzzy logic to improve the performance of the convention
al internal model controller (IMC). This controller is called FAIMCr (Fuzzy Adaptive Internal Model Controller). Twofuzzy modules plus a filter tuning equation are added to the conventional IMC to achieve the objective. The first fuzzy module, the IMCFAM, determines the process parameters changes. The second fuzzy module, the IMCFF, provides stability to the control system, and a tuning equation is developed for the filter time constant based on the process parameters. The results show the FAIMCr providing a robust response and overcoming stability problems. Adding noise to the sensor signal does not affect the performance of the FAIMC.The contributions presented in this work include:The development of a fuzzy controller with intermediate variable for cascade control purposes. An adaptive model controller which uses fuzzy logic to predict the process parameters changes for the IMC controller. An IMC filter tuning equation to update the filter time constant based in the process paramete
rs values. A variable fuzzy filter for the internal model controller (IMC) useful to provide stability to the control system.
4
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.
Full textAbstract:
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)<br>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<br>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.<br>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.
5
Mohd, Noor Mohd Junaizee. "Application of knowledge-based fuzzy inference system on high voltage transmission line maintenance." Thesis, Queensland University of Technology, 2004. https://eprints.qut.edu.au/16050/1/Mohd_Junaizee_Mohd_Noor_Thesis.pdf.
Full textAbstract:
A majority of utilities conduct maintenance of transmission line components based on the results of routine visual inspection. The inspection is normally done by inspectors who detect defects by visually checking transmission line components either from the air (in helicopters), from the ground (by using high-powered binoculars) or from the top of the structure (by climbing the structure).
The main problems with visual inspection of transmission lines are that the determination of the defects varies depending on the inspectors' knowledge and experience and that the defects are often reported qualitatively using vague and linguistic terms such as "medium crack", "heavy rust", "small deflection". As a result of these drawbacks, there is a large variance and inconsistency in defect reporting (which, in time, makes it difficult for the utility to monitor the condition of the components) leading to ineffective or wrong maintenance decisions. The use of inspection guides has not been able to fully address these uncertainties.
This thesis reports on the application of a visual inspection methodology that is aimed at addressing the above-mentioned problems. A knowledge-based Fuzzy Inference System (FIS) is designed using Matlab's Fuzzy Logic Toolbox as part of the methodology and its application is demonstrated on utility visual inspection practice of porcelain cap and pin insulators. The FIS consists of expert-specified input membership functions (representing various insulator defect levels), output membership functions (indicating the overall conditions of the insulator) and IF-THEN rules. Consistency in the inspection results is achieved because the condition of the insulator is inferred using the same knowledge-base in the FIS rather than by individual inspectors. The output of the FIS is also used in a mathematical model that is developed to suggest appropriate component replacement date.
It is hoped that the methodology that is introduced in this research will help utilities achieve better maintenance management of transmission line assets.
6
Mohd, Noor Mohd Junaizee. "Application of knowledge-based fuzzy inference system on high voltage transmission line maintenance." Queensland University of Technology, 2004. http://eprints.qut.edu.au/16050/.
Full textAbstract:
A majority of utilities conduct maintenance of transmission line components based on the results of routine visual inspection. The inspection is normally done by inspectors who detect defects by visually checking transmission line components either from the air (in helicopters), from the ground (by using high-powered binoculars) or from the top of the structure (by climbing the structure).
The main problems with visual inspection of transmission lines are that the determination of the defects varies depending on the inspectors' knowledge and experience and that the defects are often reported qualitatively using vague and linguistic terms such as "medium crack", "heavy rust", "small deflection". As a result of these drawbacks, there is a large variance and inconsistency in defect reporting (which, in time, makes it difficult for the utility to monitor the condition of the components) leading to ineffective or wrong maintenance decisions. The use of inspection guides has not been able to fully address these uncertainties.
This thesis reports on the application of a visual inspection methodology that is aimed at addressing the above-mentioned problems. A knowledge-based Fuzzy Inference System (FIS) is designed using Matlab's Fuzzy Logic Toolbox as part of the methodology and its application is demonstrated on utility visual inspection practice of porcelain cap and pin insulators. The FIS consists of expert-specified input membership functions (representing various insulator defect levels), output membership functions (indicating the overall conditions of the insulator) and IF-THEN rules. Consistency in the inspection results is achieved because the condition of the insulator is inferred using the same knowledge-base in the FIS rather than by individual inspectors. The output of the FIS is also used in a mathematical model that is developed to suggest appropriate component replacement date.
It is hoped that the methodology that is introduced in this research will help utilities achieve better maintenance management of transmission line assets.
7
Havlíček, Petr. "Spornost fuzzy logických teorií v odvozovacích systémech." Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-15839.
Full textAbstract:
This thesis focus on consistency of a specific class of fuzzy logic theories that represent certain inference system. This class of theories is defined as theories containing especially so called special axioms representing rules of modeled inference system and evaluated set of formulas representing case data. Functional approach is used to develop three popular fuzzy calculi: the Gödel logic, Łukasiewicz logic and product logic. As a language it is used the language of first order propositional fuzzy logic with valuation. To check consistency we use the concept of inconsistency degree and in Łukasiewicz logic also the principle of polar index. The concept of consistency degree is also described, but not used. Simple algorithm is developed to check consistency of theory upon the basis of inconsistency degree principle. A method of use of polar index is also described and illustrated. For each fuzzy theory a term of corresponding classical theory is defined. Then consistency of fuzzy theories and their corresponding classical theories are compared. The results of comparison are presented on the example of the ad-hoc created diagnostic inference system MEDSYS II. In the end the relation between consistency of fuzzy theory of inference system and it's corresponding theory is introduced for all three used calculi and both contradiction concepts.
8
Pavlick, Bay. "A fuzzy logic based controller to provide end-to-end congestion control for streaming media applications." [Tampa, Fla.] : University of South Florida, 2005. http://purl.fcla.edu/fcla/etd/SFE0001253.
Full text
9
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.
Full textAbstract:
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.
10
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.
Full textAbstract:
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<br>Matlab R 2007b&rdquo<br>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.
11
Munhoz, Cintia Carolina. "Proposta de criação de um sistema de análise baseado em lógica fuzzy para os critérios de geoeducação em geoparques /." Sorocaba, 2020. http://hdl.handle.net/11449/192571.
Full textAbstract:
Orientador: José Arnaldo Frutuoso Roveda<br>Resumo: No Brasil e no mundo é latente a necessidade de fazer a proteção, a ampliação e a promoção do patrimônio geológico, com a finalidade de garantir as próximas gerações acesso a este tipo de patrimônio natural, de modo econômica e ecologicamente sustentável. Diante disso, a Organização das Nações Unidas para a Educação, a Ciência e a Cultura - UNESCO criou a GGN - Global Geoparks Network, ou em sua tradução Rede Global de Geoparques - RGG. A Rede Global de Geoparques tem por função fazer a inclusão de novos parques membros, bem como, fazer a manutenção dos parques membros já existentes. Para que um parque seja postulante a membro do RGG, este deve atender uma série de requisitos, dentre as quais a Geoeducação, além disso, os parques membros de tempos em tempos devem passar por validação destes requisitos. Os parques candidatos possuem a dificuldade de elencar quais são as ações prioritárias num esforço de se tornarem membros da RGG. Como estes parques podem se tornar membros da RGG com o menor esforço. O presente trabalho se tem como objetivo apresenta uma proposta de criação um Sistema de Inferência Fuzzy (SIF) para tomada de decisões em Geoeducação, por parte dos gestores de áreas passíveis de se tornarem membros da RGG. A metodologia utilizada foi a teoria de conjuntos Fuzzy, através da criação de um sistema de inferência para geração de índices de adequação em Geoeducação das áreas candidatas. Foram criados 155 cenários para testar e validar o comportamento do sistema, o mes... (Resumo completo, clicar acesso eletrônico abaixo)<br>Mestre
12
Zegzulka, Ivo. "Aplikace fuzzy logiky při hodnocení dodavatelů firmy." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2014. http://www.nusl.cz/ntk/nusl-224446.
Full textAbstract:
This thesis deals with the design of fuzzy system that can evaluate supplier of spare parts for service. The result should be applicable to a company Iveta Šťastníková - car and tire service. Primarily it should simplify operations associated with the selection of appropriate spare parts, tools and other equipment needed to operate with car service station. First, we introduce the theoretical basis for the paper, and then we go to the present state and the analysis itself. The result is a proposed solution which should correspond to the needs of the owner.
13
Fujimoto, Rodrigo Yoshiaki. "Diagnóstico automático de defeitos em rolamentos baseado em lógica fuzzy." Universidade de São Paulo, 2005. http://www.teses.usp.br/teses/disponiveis/3/3151/tde-07012006-111536/.
Full textAbstract:
Este trabalho apresenta duas metodologias baseadas em lógica fuzzy para automatizar o diagnóstico de defeito em equipamentos mecânicos, além de fazer uma comparação de seu desempenho utilizando um caso experimental. As duas metodologias estudadas são: o sistema de inferência fuzzy e o algoritmo baseado em Fuzzy C-Means. O alarme estatístico é uma metodologia existente atualmente na indústria com este objetivo e que será utilizado neste trabalho para comparação de desempenho. Para realizar os testes, foram desenvolvidos programas que permitiram criar alarmes e sistemas fuzzy utilizando um banco de dados experimental. De modo diferente ao que são feitos normalmente, os sistemas fuzzy de diagnóstico testados neste trabalho foram construídos automaticamente utilizando informações do banco de dados experimentais composto por sinais de vibração, que representam a condição normal e diversos tipos de defeitos em mancais de rolamentos. Os parâmetros escalares característicos necessários para a entrada nos sistemas fuzzy foram obtidos através do processamento dos sinais de vibração de mancais de rolamentos. Nas análises realizadas neste trabalho, foi estudada a influência de diversos características de criação do sistema fuzzy. Como exemplo, pode-se citar como principal influência, a complexidade do banco de dados a ser analisado pelo sistema fuzzy. Por fim, além de apresentar uma comparação de performance entre as metodologias fuzzy apresentadas no trabalho, com o alarme estatístico, são discutidas as características de cada uma destas metodologias. Destacam-se como principais contribuições deste trabalho, a obtenção de uma metodologia utilizada para criar de maneira automática o sistema de inferência fuzzy e as modificações realizadas no algoritmo Fuzzy C-Means para aperfeiçoar o desempenho em classificação de defeitos.<br>This works describes two proposed methodologies for the automatic diagnoses in mechanical equipment: the fuzzy system inference and a Fuzzy C-Means based algorithm. Their performances are evaluated in an experimental case and, afterwards, also compared by the statistical alarm, a diagnostic methodology very used in industries at present. In order to do the tests, a developed computer algorithm allowed creating alarms and fuzzy systems by the use of an experimental database. These tested diagnostic systems were automatically built using information from the mentioned database that was composed by samples of vibration signals, representing several types of rolling bearing defects and the bearing normal condition. The fuzzy systems input scalar parameters were obtained by signal processing. The influence of some of the building fuzzy systems parameters in the system performance was also studied, which allow establishing, for example, that the database complexity is an important factor in the fuzzy system performance. Finally, this work discusses the main characteristics of each one of the described methodologies. The most important contribution of this work is the proposition of a methodology for creating fuzzy system automatically as well as the analysis of the fuzzy C-Means as a tool for system diagnoses.
14
Ahnlén, Fredrik. "Automatic Detection of Low Passability Terrain Features in the Scandinavian Mountains." Thesis, KTH, Geodesi och satellitpositionering, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254709.
Full textAbstract:
During recent years, much focus have been put on replacing time consuming manual mappingand classification tasks with automatic methods, having minimal human interaction. Now it ispossible to quickly classify land cover and terrain features covering large areas to a digital formatand with a high accuracy. This can be achieved using nothing but remote sensing techniques,which provide a far more sustainable process and product. Still, some terrain features do not havean established methodology for high quality automatic mapping.The Scandinavian Mountains contain several terrain features with low passability, such asmires, shrub and stony ground. It would be of interest to anyone passing the land to avoid theseareas. However, they are not sufficiently mapped in current map products.The aim of this thesis was to find a methodology to classify and map these terrain featuresin the Scandinavian Mountains with high accuracy and minimal human interaction, using remotesensing techniques. The study area chosen for the analysis is a large valley and mountain sidesouth-east of the small town Abisko in northern Sweden, which contain clearly visible samplesof the targeted terrain features. The methodology was based on training a Fuzzy Logic classifierusing labeled training samples and descriptors derived from ortophotos, LiDAR data and currentmap products, chosen to separate the classes from each other by their characteristics. Firstly,a set of candidate descriptors were chosen, from which the final descriptors were obtained byimplementing a Fisher score filter. Secondly a Fuzzy Inference System was constructed usinglabeled training data from the descriptors, created by the user. Finally the entire study area wasclassified pixel-by-pixel by using the trained classifier and a majority filter was used to cluster theoutputs. The result was validated by visual inspection, comparison to the current map productsand by constructing Confusion Matrices, both for the training data and validation samples as wellas for the clustered- and non-clustered results.The results showed that<br>De senaste åren har mycket fokus lagts på att ersätta tidskrävande manuella karterings- och klassificeringsmetodermed automatiserade lösningar med minimal mänsklig inverkan. Det är numeramöjligt att digitalt klassificera marktäcket och terrängföremål över stora områden, snabbt och medhög noggrannhet. Detta med hjälp av enbart fjärranalys, vilket medför en betydligt mer hållbarprocess och slutprodukt. Trots det finns det fortfarande terrängföremål som inte har en etableradmetod för noggrann automatisk kartering.Den skandinaviska fjällkedjan består till stor del av svårpasserade terrängföremål som sankmarker,videsnår och stenig mark. Alla som tar sig fram i terrängen obanat skulle ha nytta av attkunna undvika dessa områden men de är i nuläget inte karterade med önskvärd noggrannhet.Målet med denna analys var att utforma en metod för att klassificera och kartera dessa terrängföremåli Skanderna, med hög noggrannhet och minimal mänsklig inverkan med hjälp avfjärranalys. Valet av testområde för analysen är en större dal och bergssida sydost om Abisko inorra Sverige som innehåller tydliga exemplar av alla berörda terrängföremål. Metoden baseradespå att träna en Fuzzy Logic classifier med manuellt utvald träningsdata och deskriptorer,valda för att bäst separera klasserna utifrån deras karaktärsdrag. Inledningsvis valdes en uppsättningav kandidatdeskriptorer som sedan filtrerades till den slutgiltiga uppsättningen med hjälp avett Fisher score filter. Ett Fuzzy Inference System byggdes och tränades med träningsdata fråndeskriptorerna vilket slutligen användes för att klassificera hela testområdet pixelvis. Det klassificeraderesultatet klustrades därefter med hjälp av ett majoritetsfilter. Resultatet validerades genomvisuell inspektion, jämförelse med befintliga kartprodukter och genom confusion matriser, vilkaberäknades både för träningsdata och valideringsdata samt för det klustrade och icke-klustraderesultatet.Resultatet visade att de svårpasserade terrängföremålen sankmark, videsnår och stenig markkan karteras med hög noggrannhet med hjälp denna metod och att resultaten generellt är tydligtbättre än nuvarande kartprodukter. Däremot kan metoden finjusteras på flera plan för att optimeras.Bland annat genom att implementera deskriptorer för markvattenrörelser och användandeav LiDAR med högre spatial upplösning, samt med ett mer fulltäckande och spritt val av klasser.
15
Antong, Hasmawati P. "Highly redundant and fault tolerant actuator system : control, condition monitoring and experimental validation." Thesis, Loughborough University, 2017. https://dspace.lboro.ac.uk/2134/32792.
Full textAbstract:
This thesis is concerned with developing a control and condition monitoring system for a class of fault tolerant actuators with high levels of redundancy. The High Redundancy Actuator (HRA) is a concept inspired by biomimetics that aims to provide fault tolerance using relatively large numbers of actuation elements which are assembled in parallel and series configurations to form a single actuator. Each actuation element provides a small contribution to the overall force and displacement of the system. Since the capability of each actuation element is small, the effect of faults within the individual element of the overall system is also small. Hence, the HRA will gracefully degrade instead of going from fully functional to total failure in the presence of faults. Previous research on HRA using electromechanical technology has focused on a relatively low number of actuation elements (i.e. 4 elements), which were controlled with multiple loop control methods. The objective of this thesis is to expand upon this, by considering an HRA with a larger number of actuation elements (i.e. 12 elements). First, a mathematical model of a general n-by-m HRA is derived from first principles. This method can be used to represent any size of electromechanical HRA with actuation elements arranged in a matrix form. Then, a mathematical model of a 4-by-3 HRA is obtained from the general n-by-m model and verified experimentally using the HRA test rig. This actuator model is then used as a foundation for the controller design and condition monitoring development. For control design, two classical and control method-based controllers are compared with an H_infinity approach. The objective for the control design is to make the HRA track a position demand signal in both health and faulty conditions. For the classical PI controller design, the first approach uses twelve local controllers (1 per actuator) and the second uses only a single global controller. For the H_infinity control design, a mixed sensitivity functions is used to obtain good tracking performance and robustness to modelling uncertainties. Both of these methods demonstrate good tracking performance, with a slower response in the presence of faults. As expected, the H_infinity control method's robustness to modelling uncertainties, results in a smaller performance degradation in the presence of faults, compared with the classical designs. Unlike previous work, the thesis also makes a novel contribution to the condition monitoring of HRA. The proposed algorithm does not require the use of multiple sensors. The condition monitoring scheme is based on least-squares parameter estimation and fuzzy logic inference. The least-squares parameter estimation estimates the physical parameters of the electromechanical actuator based on input-output data collected from real-time experiments, while the fuzzy logic inference determines the health condition of the actuator based on the estimated physical parameters. Hence, overall, a new approach to both control and monitoring of an HRA is proposed and demonstrated on a twelve elements HRA test rig.
16
Ronzhina, Marina. "Klasifikace mikrospánku analýzou EEG." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-217965.
Full textAbstract:
This master thesis deals with detection of microsleep on the basis of the changes in power spectrum of EEG signal. The results of time-frequency analysis are input values for the classifikation. Proposed classification method uses fuzzy logic. Four classifiers were designed, which are based on a fuzzy inference systems, that are differ in rule base. The results of fuzzy clustering are used for the design of rule premises membership functions. The two classifiers microsleep detection use only alpha band of the EEG signal’s spectrogram then allows the detection of the relaxation state of a person. Unlike to first and second classifiers, the third classifier is supplemented with rules for the delta band, which makes it possible to distinguish the 3 states: vigilance, relaxation and somnolence. The fourth classifier inference system includes the rules for the whole spectrum band. The method was implemented by computer. The program with a graphical user interface was created.
17
Arsava, Kemal Sarp. "Modeling, Control and Monitoring of Smart Structures under High Impact Loads." Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-dissertations/105.
Full textAbstract:
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.
18
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.
Full textAbstract:
"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."
19
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.
Full textAbstract:
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.
20
Yaguinuma, Cristiane Akemi. "Processamento de conhecimento impreciso combinando raciocínio de ontologias fuzzy e sistemas de inferência fuzzy." Universidade Federal de São Carlos, 2013. https://repositorio.ufscar.br/handle/ufscar/288.
Full textAbstract:
Made available in DSpace on 2016-06-02T19:03:58Z (GMT). No. of bitstreams: 1
5694.pdf: 2329501 bytes, checksum: 90a80d78f180e25fc719ec410704ff8f (MD5)
Previous issue date: 2013-12-13<br>Financiadora de Estudos e Projetos<br>In Computer Science, ontologies are used for knowledge representation in a number of applications, aiming to structure and handle domain semantics through models shared by humans and computational systems. Although traditional ontologies model semantic information and support reasoning tasks, they are based on a formalism which is less suitable to express the vagueness inherent in real-world phenomena and human language. To address this issue, many proposals investigate how traditional ontologies can be extended by incorporating concepts from fuzzy sets and fuzzy logic, resulting in fuzzy ontologies. In special, combining the formalism from fuzzy ontologies with fuzzy rule-based reasoning, which has been successfully applied in the context of fuzzy inference systems, can lead to more expressive inferences involving imprecision. In this sense, this doctoral thesis aims at exploring the integration of fuzzy ontology reasoning with fuzzy inference systems, resulting in the definition and the development of two approaches: HyFOM (Hybrid integration of Fuzzy Ontology and Mamdani reasoning) and FT-FIS (Fuzzy Tableau and Fuzzy Inference System). HyFOM is based on a hybrid architecture combining reasoners for ontologies, fuzzy ontologies and fuzzy inference systems, focusing on the interaction among its independent components. FT-FIS defines an interface between a fuzzy tableau-based algorithm and a fuzzy inference system, including the fuzzyRuleReasoning predicate that allows fuzzy rule-based reasoning to be invoked whenever necessary for fuzzy ontology reasoning tasks. The main contribution of HyFOM and FT-FIS comes from their reasoning architectures, which combine flexibility in terms of fuzzy rule semantics with the collaboration between inferences from both types of reasoning. Experiments regarding the recommendation of touristic attractions, based on synthetic data, revealed that HyFOM and FT-FIS provide integrated inferences, in addition to a more expressive approximation of the relation defined by fuzzy rules than the results from the fuzzyDL reasoner. In experiments involving the evaluation of chemical risk in food samples, based on real data, results obtained by HyFOM and FT-FIS are also more precise than fuzzyDL results, in comparison with reference values available in this domain.<br>No contexto da Ciência da Computação, ontologias são utilizadas para representação de conhecimento em diversas aplicações, com o intuito de estruturar e tratar a semântica de domínios específicos. Embora representem e permitam inferir conhecimento implícito, as ontologias convencionais baseiam-se em um formalismo que não é capaz de expressar a imprecisão presente em fenômenos do mundo real e na linguagem humana. Para abordar esta limitação, há diversas pesquisas que investigam a incorporação de conceitos da teoria de conjuntos fuzzy e da lógica fuzzy em ontologias, resultando em ontologias fuzzy. Em especial, combinar o formalismo das ontologias fuzzy com o raciocínio baseado em regras fuzzy, utilizado com sucesso no contexto de sistemas de inferência fuzzy, pode proporcionar uma maior expressividade com relação às inferências envolvendo imprecisão. Neste sentido, o objetivo deste projeto de doutorado é explorar a integração do raciocínio de ontologias fuzzy e de sistemas de inferência fuzzy, resultando na definição e no desenvolvimento das abordagens HyFOM (Hybrid integration of Fuzzy Ontology and Mamdani reasoning) e FT-FIS (Fuzzy Tableau and Fuzzy Inference System). HyFOM baseia-se em uma arquitetura híbrida que combina motores de inferência existentes na literatura para ontologias, ontologias fuzzy e sistemas de inferência fuzzy, com foco na interação entre seus componentes independentes. FT-FIS define uma interface entre um algoritmo baseado em tableau fuzzy e um sistema de inferência fuzzy, incluindo o predicado fuzzyRuleReasoning que permite invocar o raciocínio baseado em regras fuzzy quando for necessário para as tarefas de raciocínio da ontologia fuzzy. A principal contribuição das arquiteturas de raciocínio de HyFOM e FT-FIS está na combinação de flexibilidade, em termos da semântica das regras fuzzy, com a colaboração entre as inferências de ambos tipos de raciocínio. Experimentos considerando a recomendação de atrações turísticas, baseados em dados sintéticos, revelaram que HyFOM e FT-FIS são capazes de proporcionar inferências integradas, além de uma aproximação mais expressiva da relação estabelecida pelas regras fuzzy que os resultados providos pelo raciocinador fuzzyDL. Em experimentos envolvendo o domínio de risco químico em alimentos, baseado em dados reais, os resultados de HyFOM e FT-FIS também são mais precisos que os resultados de fuzzyDL, em comparação com valores de referência disponíveis nesse domínio.
21
Павловський, Владислав Олександрович. "Система керування дорожнім рухом у місті". Master's thesis, КПІ ім. Ігоря Сікорського, 2019. https://ela.kpi.ua/handle/123456789/32305.
Full textAbstract:
Інтенсивне збільшення кількості транспортних засобів у містах України за останні роки призвело до значного перевантаження транспортної мережі, утворення транспортних заторів. Хоча виникнення заторів обумовлено багатьма факторами, значна їх кількість сконцентрована на регульованих перехрестях.
Метою даної роботи є підвищення ефективності керування дорожнім рухом у місті. Особлива увага приділяється керуванню світлофорними об’єктами з використанням сучасного апарата нечіткої логіки.
Розроблено структуру побудови системи керування дорожнім рухом в місті. Виділено підсистеми реєстрації дорожнього руху, світлофорного регулювання, керування зовнішнім освітленням. В такому випадку система легко модернізується і масштабується.
Проведено дослідження транспортного потоку. Побудовано математичну модель нечіткого логічного виведення для гнучкого регулювання тривалості фаз двохфазного світлофорного об’єкта.<br>In recent years, the intensive increase of vehicles in the cities of Ukraine has led to significant overload of the transport network and formation of traffic jams. Although congestion is caused by many factors, many of them are concentrated at controlled intersections.
The purpose of this work is to improve the efficiency of road traffic control in the city. Special attention is paid to the management of traffic light objects using modern apparatus of indistinct logic.
The structure of construction of traffic management system in the city has been developed. The subsystems of traffic registration, traffic lights, outdoor lighting control have been highlighted. In this case, the system is easily upgraded and scalable.
The study of traffic flow was conducted. The mathematical model of fuzzy inference system for flexible phase duration control of two-phase traffic light object was constructed.
22
Chen, Guiming. "Fuzzy FOIL: A fuzzy logic based inductive logic programming system." Thesis, University of Ottawa (Canada), 1996. http://hdl.handle.net/10393/9621.
Full textAbstract:
In many domains, characterizations of a given attribute are imprecise, uncertain and incomplete in the available learning examples. The definitions of classes may be vague. Learning systems are frequently forced to deal with such uncertainty. Traditional learning systems are designed to work in the domains where imprecision and uncertainty in the data are absent. Those learning systems are limited because of their impossibility to cope with uncertainty--a typical feature of real-world data. In this thesis, we developed a fuzzy learning system which combines inductive learning with a fuzzy approach to solve problems arising in learning tasks in the domains affected by uncertainty and vagueness. Based on Fuzzy Logic, rather than pure First Order Logic used in FOIL, this system extends FOIL with learning fuzzy logic relation from both imprecise examples and background knowledge represented by Fuzzy Prolog. The classification into the positive and negative examples is allowed to be a degree (of positiveness or negativeness) between 0 and 1. The values of a given attribute in examples need not to be the same type. Symbolic and continuous data can exist in the same attribute, allowing for fuzzy unification (inexact matching). An inductive learning problem is formulated as to find a fuzzy logic relation with a degree of truth, in which a fuzzy gain calculation method is used to guide heuristic search. The Fuzzy FOIL's ability of learning the required fuzzy logic relations and dealing with vague data enhances FOIL's usefulness.
23
Whiter, A. M. "Pi-fuzzy Logic (and its application to open-world inference)." Thesis, University of Bristol, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378779.
Full text
24
Weeraprajak, Issarest. "Faster Adaptive Network Based Fuzzy Inference System." Thesis, University of Canterbury. Mathematics and Statistics, 2007. http://hdl.handle.net/10092/1234.
Full textAbstract:
It has been shown by Roger Jang in his paper titled "Adaptive-network-based fuzzy inference systems" that the Adaptive Network based Fuzzy Inference System can model nonlinear functions, identify nonlinear components in a control system, and predict a chaotic time series. The system use hybrid-learning procedure which employs the back-propagation-type gradient descent algorithm and the least squares estimator to estimate parameters of the model. However the learning procedure has several shortcomings due to the fact that * There is a harmful and unforeseeable influence of the size of the partial derivative on the weight step in the back-propagation-type gradient descent algorithm. *In some cases the matrices in the least square estimator can be ill-conditioned. *Several estimators are known which dominate, or outperform, the least square estimator. Therefore this thesis develops a new system that overcomes the above problems, which is called the "Faster Adaptive Network Fuzzy Inference System" (FANFIS). The new system in this thesis is shown to significantly out perform the existing method in predicting a chaotic time series , modelling a three-input nonlinear function and identifying dynamical systems. We also use FANFIS to predict five major stock closing prices in New Zealand namely Air New Zealand "A" Ltd., Brierley Investments Ltd., Carter Holt Harvey Ltd., Lion Nathan Ltd. and Telecom Corporation of New Zealand Ltd. The result shows that the new system out performed other competing models and by using simple trading strategy, profitable forecasting is possible.
25
Xu, Andong. "Flexible adaptive-network-based fuzzy inference system." Diss., Online access via UMI:, 2006.
Find full textAbstract:
Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Dept. of Systems Science and Industrial Engineering, 2006.<br>Includes bibliographical references.
26
Hariri, Ali. "An adaptive fuzzy logic power system stabilizer." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq20741.pdf.
Full text
27
Mian, Tariq M. "Fuzzy Logic based Automotive Airbag Control System." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0011/MQ52612.pdf.
Full text
28
Kutuva, Shanthanand R. "A Fuzzy Logic Based Virtual Surgery System." University of Akron / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=akron1153967254.
Full text
29
Targhetta, Walter <1994>. "A Trading system based on fuzzy logic." Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/17959.
Full text
30
Zheng, Man. "Management of an intelligent argumentation network for a web-based collaborative engineering design environment." Diss., Rolla, Mo. : University of Missouri-Rolla, 2007. http://scholarsmine.umr.edu/thesis/pdf/Zheng_09007dcc803e416f.pdf.
Full textAbstract:
Thesis (M.S.)--University of Missouri--Rolla, 2007.<br>Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed April 22, 2008) Includes bibliographical references (p. 33-35).
31
Vural, Ozgur Ahmet. "Fuzzy Logic Guidance System Design For Guided Missiles." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1026715/index.pdf.
Full textAbstract:
This thesis involves modeling, guidance, control, and flight simulations of a canard
controlled guided missile.
The autopilot is designed by a pole placement technique. Designed autopilot is
used with the guidance systems considered in the thesis.
Five different guidance methods are applied in the thesis, one of which is the
famous proportional navigation guidance. The other four guidance methods are
different fuzzy logic guidance systems designed considering different types of
guidance inputs.
Simulations are done against five different target types and the performances of the
five guidance methods are compared and discussed.
32
Guner, Evren. "Adaptive Neuro Fuzzy Inference System Applications In Chemical Processes." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1252246/index.pdf.
Full textAbstract:
Neuro-Fuzzy systems are the systems that neural networks (NN) are incorporated in fuzzy systems, which can use knowledge automatically by learning algorithms of NNs. They can be viewed as a mixture of local experts. Adaptive Neuro-Fuzzy inference system (ANFIS) is one of the examples of Neuro Fuzzy systems in which a fuzzy system is implemented in the framework of adaptive networks. ANFIS constructs an input-output mapping based both on human knowledge (in the form of fuzzy rules) and on generated input-output data pairs.
Effective control for distillation systems, which are one of the important unit operations for chemical industries, can be easily designed with the known composition values. Online measurements of the compositions can be done using direct composition analyzers. However, online composition measurement is not feasible, since, these analyzers, like gas chromatographs, involve large measurement delays. As an alternative, compositions can be estimated from temperature measurements. Thus, an online estimator that utilizes temperature measurements can be used to infer the produced compositions. In this study, ANFIS estimators are designed to infer the top and bottom product compositions in a continuous distillation column and to infer the reflux drum compositions in a batch distillation column from the measurable tray temperatures. Designed estimator performances are further compared with the other types of estimators such as NN and Extended Kalman Filter (EKF).
In this study, ANFIS performance is also investigated in the adaptive Neuro-Fuzzy control of a pH system. ANFIS is used in specialized learning algorithm as a controller. Simple ANFIS structure is designed and implemented in adaptive closed loop control scheme. The performance of ANFIS controller is also compared with that of NN for the case under study.
33
Aguiar, Eduardo Pestana de. "Fuzzy logic system applied to classification problems in railways." Universidade Federal de Juiz de Fora (UFJF), 2016. https://repositorio.ufjf.br/jspui/handle/ufjf/3627.
Full textAbstract:
Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-03-10T12:31:18Z
No. of bitstreams: 1
eduardopestanadeaguiar.pdf: 7884545 bytes, checksum: 182caace21281f7afce6554505811116 (MD5)<br>Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-03-13T17:18:31Z (GMT) No. of bitstreams: 1
eduardopestanadeaguiar.pdf: 7884545 bytes, checksum: 182caace21281f7afce6554505811116 (MD5)<br>Made available in DSpace on 2017-03-13T17:18:31Z (GMT). No. of bitstreams: 1
eduardopestanadeaguiar.pdf: 7884545 bytes, checksum: 182caace21281f7afce6554505811116 (MD5)
Previous issue date: 2016-09-26<br>-<br>This thesis presents new fuzzy models applied to classification problems. With this regards,
we introduce the use of set-membership concept, derived from the adaptive filter
theory, into the training procedure of type-1 and singleton/non-singleton fuzzy logic systems,
in order to reduce computational complexity and to increase convergence speed.
Also, we present different criteria for using together with set-membership. Furthermore,
we discuss the usefulness of delta rule delta, local Lipschitz estimation, variable step size
and variable step size adaptive algorithms to yield additional improvement in terms of
computational complexity reduction and convergence speed. Another important contribution
of this thesis is to address the height type-reduction and to propose a modified
version of interval singleton type-2 fuzzy logic system, so−called upper and lower singleton
type-2 fuzzy logic system. The obtained results are compared with other models
reported in the literature, demonstrating the effectiveness of the proposed classifiers and
revealing that the proposals are able to properly handle with uncertainties associated with
the measurements and with the data that are used to tune the parameters of the model.
Based on data set provided by a Brazilian railway company, the models outlined above are
applied in the classification of three possible faults and the normal condition of the switch
machine, which is an equipment used for handling railroad switches. Finally, this thesis
discusses the use of set-membership concept into the training procedure of an interval
and singleton type-2 fuzzy logic system and of an upper and lower singleton type-2 fuzzy
logic system, aiming to reduce computational complexity and to increase the convergence
speed and the classification ratio. Also, we discuss the adoption of different criteria together
with set-membership based-techniques. The performance is based on the data set
composed of images provided by the same Brazilian railway company, which covers the
four possible rail head defects and the normal condition of the rail head. The reported results
show that the proposed models result in improved convergence speed, slightly higher
classification ratio and remarkable computation complexity reduction when we limit the
number of epochs for training, which may be required due to real time constraint or low
computational resource availability.
34
Castro, León Iván. "Type-2 fuzzy logic system applications for power systems." Thesis, University of Newcastle upon Tyne, 2017. http://hdl.handle.net/10443/3816.
Full textAbstract:
In the move towards ubiquitous information & communications technology, an opportunity for further optimisation of the power system as a whole has arisen. Nonetheless, the fast growth of intermittent generation concurrently with markets deregulation is driving a need for timely algorithms that can derive value from these new data sources. Type-2 fuzzy logic systems can offer approximate solutions to these computationally hard tasks by expressing non-linear relationships in a more flexible fashion. This thesis explores how type-2 fuzzy logic systems can provide solutions to two of these challenging power system problems; short-term load forecasting and voltage control in distribution networks. On one hand, time-series forecasting is a key input for economic secure power systems as there are many tasks that require a precise determination of the future short-term load (e.g. unit commitment or security assessment among others), but also when dealing with electricity as commodity. As a consequence, short-term load forecasting becomes essential for energy stakeholders and any inaccuracy can be directly translated into their financial performance. All these is reflected in current power systems literature trends where a significant number of papers cover the subject. Extending the existing literature, this work focuses in how these should be implemented from beginning to end to bring to light their predictive performance. Following this research direction, this thesis introduces a novel framework to automatically design type-2 fuzzy logic systems. On the other hand, the low-carbon economy is pushing the grid status even closer to its operational limits. Distribution networks are becoming active systems with power flows and voltages defined not only by load, but also by generation. As consequence, even if it is not yet absolutely clear how power systems will evolve in the long-term, all plausible future scenarios claim for real-time algorithms that can provide near optimal solutions to this challenging mixed-integer non-linear problem. Aligned with research and industry efforts, this thesis introduces a scalable implementation to tackle this task in divide-and-conquer fashion.
35
Khalil, Azher Othamn K. "Fuzzy logic control and navigation of mobile vehicles." Thesis, University of Newcastle Upon Tyne, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.323486.
Full text
36
Guo, Lizhu. "Key optimization issues for the design of fuzzy-inference system." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ35169.pdf.
Full text
37
Bell, K. R. W. "Artificial intelligence and uncertainty in power system operation." Thesis, University of Bath, 1995. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336238.
Full text
38
SU, WEN-JING, and 蘇文進. "The logic implementation and system verification of a high performance fuzzy inference engine." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/06529096200354557141.
Full text
39
Tsou, Chih-Ning, and 鄒治寧. "Realization of A Small-scale Greenhouse Monitoring and Control System Using Fuzzy Inference Logic." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/16478415115943351331.
Full textAbstract:
碩士<br>國立臺灣海洋大學<br>機械與機電工程學系<br>102<br>The most important condition to have a good crop yield and quality is the environmental parameters and culture techniques, while the greenhouse is a good way to control the environmental parameters. Taiwan's high economic crops are often used in greenhouse cultivation. Automated greenhouse comprising a sensing and control, sensing for monitoring various environmental parameters, and the control is based on the default control method to action. Automation can significantly reduce manual labor and precise control of environmental parameters. Such as temperature and humidity needed to achieve the purpose of crop growth environment. In this thesis, the design of a small-scale greenhouse monitoring and control system that uses temperature, humidity, soil moisture, light intensity and variety of sensors and fan, light bulbs, heating wire, sprinkler and other actuators.
Combines the Arduino with the LabVIEW and MATLAB Fuzzy Toolbox to realize a small greenhouse monitoring and control model. Not only can Real Time observe the temperature, humidity, soil moisture, or light sensing data for each degree change in circumstances in different times, but also to understand the current state of various actuators operate on the front panel of the chart.
In the experiment using the On / Off with Hysteresis Dead Band control, P control, PID control, and Fuzzy Control four control laws. Observed temperature, humidity, soil moisture and sunlight and other variables of response and compare their advantages and disadvantages. If we can accurately measure environmental parameters and controls properly, you can create a conducive environment for the growth of crops.
In this study, completed a small automatic monitoring and control greenhouse model at a low cost, which can effectively and quickly control the desired growth of plant cultivation environment to achieve increased crop yields and reduce farming costs.
Experimental results show that P control, PID control, fuzzy control in the greenhouse temperature and humidity control results are within a reasonable range. Where P controller combines the advantages of a good control performance and easy to implement, the actual needs of greenhouse environment control is enough.
40
Kuo, Hsin-Hsien, and 郭信賢. "A Research of Fuzzy Logic Inference System on Logistics Training of Army Military School." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/53935912749900860155.
Full textAbstract:
碩士<br>國防大學管理學院<br>資源管理及決策研究所<br>99<br>This study was integrated the Delphi method with the fuzzy inference system to develop logistics training system architecture of the Mergence of the Military Schools. This architecture includes 5 criteria and 19 successful critical factors. And then, this study used the fuzzy inference system to make sensitivity analysis of the criteria and factors. This study can provide decision makers to view the current training processes and training objective. At same time, it can promote the overall purpose of the training by using of adapted correct the training mode and projects.
In practice, the logistics training system could help decision makers and school staff to understand the whole process of logistics training. In academic, it can provide a different direction of thinking by using of the relationship with each other and the feedback information of logistics training system. After using the fuzzy inference system, this decision makers and school staff can more clearly understand the logistics training system. Therefore, this study hopes to collect the more training parameters in the future, so that the training system will be more comprehensive.
41
Patel, Pretesh Bhoola. "A forecasting of indices and corresponding investment decision making application." Thesis, 2007. http://hdl.handle.net/10539/2191.
Full textAbstract:
Student Number : 9702018F -
MSc(Eng) Dissertation -
School of Electrical and Information Engineering -
Faculty of Engineering and the Built Environment<br>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.
42
Wang, Yu-Hsuan, and 王宇軒. "Application of a scooter fault diagnosis system using fuzzy-logic inference and neural networks with adaptive order tracking technique." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/93045823007896041429.
Full textAbstract:
碩士<br>國立彰化師範大學<br>車輛與軌道技術研究所<br>94<br>In the present study, a fault diagnosis system using acoustic emission with adaptive order tracking technique, fuzzy-logic interference and neural networks for scooter platform is described. Order tracking of acoustic or vibration signal is a well-known technique that can be used for fault diagnosis of rotating machinery. Unfortunately, most of the conventional order-tracking methods are primarily based on Fourier analysis with the revolution of the machinery, the frequency smearing effect is often arises in some critical conditions. In the present study, the order tracking problem is treated as the tracking of frequency-varying bandpass signals and the order amplitudes can be calculated with high resolution. The order amplitude figures are then used for creating the data bank in the proposed intelligent fault diagnosis system. A fuzzy-logic inference is proposed to develop the diagnostic rules of data base in the present fault diagnosis system. Another technique of artificial neural network using back-propagation network and radial basis function network is proposed to develop the artificial neural network for fault diagnosis system. The experimental works are carried to evaluate the effect of proposed system for fault diagnosis in a scooter platform under various operation conditions. The experimental results indicated that the proposed system is effective for increasing accuracy in fault diagnosis of scooters.
43
CAPILLO, Antonino. "Optimal energy management and performance evaluation of an Integrated Mobility System: the "Life for Silver Coast" case study." Doctoral thesis, 2021. http://hdl.handle.net/11573/1565312.
Full textAbstract:
Nowdays, Climate Change and Global Warming are very relevant issues and
Humankind relies on Renewable Energy Sources (RESs) for mitigating
environmental impacts. RESs exploitation implies the adoption of a
Distributed Energy Generation (DEG), implemented through local electrical
grids called Microgrids (MGs). The intent of harvesting as much as energy
possible, dealing with the RESs unpredictable nature, makes researchers
develop suitable ICT systems (Energy Management Systems or EMSs). Smart
Grids (SGs) are systems composed of many MGs, thanks to which a whole
urban area can perform an efficient energy management. Energy
Communities, made up of companies, research centres and Universities strive
to design and realize SGs, in a sustainable development vision.
In this context, the sustainable mobility system realized in the "LIFE for
Silver Coast" European Project is a very good test bench for EMSs synthesis.
In fact, Electric Vehicles (EVs) and charging stations will be integrated in the
Project Area and managed through proprietary EMSs. In addition, the
achieved knowhow can be used by the Energy Community to develop Smart
Grids, not only in the same area.
In this thesis, the Evolutionary Fuzzy System (EFS) paradigm is applied for
the synthesis of an EMS. In particular, a double-step optimization
Hierarchical Genetic Algorithm (HGA) procedure is implemented for
reducing the computational cost. The resulting Fuzzy Inference System-
Genetic Algorithm (FIS-GA) is tested for the onboard optimal energy
management of the LIFE "Valentino" Class e-boat, with the purpose of
implementing the same EMS in a residential MG. In addition, an application
based on Life Quality indicators related to mobility systems is presented.
44
Sibiya, Malusi. "A fuzzy logic micro-controller enabled system for the monitoring of micro climatic parameters of a greenhouse." Diss., 2017. http://hdl.handle.net/10500/24874.
Full textAbstract:
Motivation behind this master dissertation is to introduce a novel study called " A fuzzy logic micro-controller enabled system for the monitoring of micro-climatic parameters of a greenhouse" which is capable of intelligently monitoring and controlling the greenhouse climate conditions in a preprogrammed manner.
The proposed system consists of three stations: Sensor Station, Coordinator Station, and Central Station. To allow for better monitoring of the climate condition in the greenhouse, fuzzy logic controller is embedded in the system as the system becomes more intelligent with fuzzy decision making. The sensor station is equipped with several sensor elements such as MQ-7 (Carbon monoxide sensor), DHT11 (Temperature and humidity sensor), LDR (light sensor), grove moisture sensor (soil moisture sensor). The communication between the sensor station and the coordinator station is achieved through XBee wireless modules connected to the Arduino Mega and the communication between coordinator station and the central station is also achieved via XBee wireless modules connected to the Arduino Mega.
The experiments and tests of the system were carried out at one of IKHALA TVET COLLEGE’s greenhouses that is used for learning purposes by students studying agriculture at the college. The purpose of conducting the experiments at the college’s green house was to determine the functionality and reliability of the designed wireless sensor network using ZigBee wireless technology. The experiment result indicated that XBee modules could be used as one solution to lower the installation cost, increase flexibility and reliability and create a greenhouse management system that is only based on wireless nodes. The experiment result also showed that the system became more intelligent if fuzzy logic was used by the system for decision making.
The overall system design showed advantages in cost, size, power, flexibility and intelligence. It is trusted that the results of the project will give the chance for further research and development of a low cost greenhouse monitoring system for commercial use.<br>Electrical and Mining Engineering<br>M. Tech. (Electrical Engineering)
45
Nguyen, Huy Huynh. "A neural fuzzy approach to modeling the thermal behavior of power transformers." Thesis, 2007. https://vuir.vu.edu.au/1495/.
Full textAbstract:
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.
46
Chang, Ming-Hsiang, and 張銘顯. "A High Performance Parallel Fuzzy Logic Inference Engine Chip Design." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/44453453136173901773.
Full textAbstract:
碩士<br>國立成功大學<br>電機工程研究所<br>81<br>Fuzzy logic is very popular and welcome today.Its applications
cover a wide range from decision making command and analysis
areas to intelligent controllers for real-time processes. Since
the complexity of problems is increased enormously,
implementing fuzzy inference engines on VLSI chips for real-
time approximate reasoning is more imperative. In previous
chips, there has been no attempt to improve the hardware
implementation for cost or speed, and the resulting designs
become too slow or cost too much. In the view of the fact that
only a few rules will be fired in each cycle of inference,
therefore, only a few data paths are implemented in the chip,
thus reducing the hardware cost significantly. Moreover, the
defuzzification process is accelerated by using the table look-
up approach, thus improving the performance of the chip
greatly. In the thesis, we present a high performance fuzzy
inference engine chip.It is implemented by the high-level
prototype validation in Synopsys VHDL simulation system and the
low-level chip implementation in Genesil system. The
performance of our chip is up to 225k FLIPS and down to 133k
FLIPS with 49 rules per inference, which is fast enough in
various applications. The area of chip is 0.913 cm by 1.027 cm.
The chip is implemented with the 1.2 um technology of TSMC.
47
Shu, Sue-Ping, and 徐淑平. "A New Approach to Fuzzy Logic Inference Based on Spatial Relationship of Fuzzy Subsets." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/27666831959965319502.
Full textAbstract:
碩士<br>國立清華大學<br>資訊科學研究所<br>84<br>In the study, a new inference method which match the intuition of humanreasoning i sproposed. For each fuzy rule "if x is A, then y is B" the roposed inference method produces an output fuzzy subset B' according to the spatial relationship of input fuzzy subset A' and the antecedent A. The membership function of the output fuzzy subset B' is of the same shape as that of the input fuzzy subset A'. Moreover, the spatial relationship of B' is consistent with that of A and A'. Experiments on a simple single-input single-output example and the truck backer-parking control are performed to compare our proposed inference method with the Mamdani's method. These experiments show that our results compares favorably with that of the Mamdani's method.
48
Chen, Hung-Pin, and 陳弘斌. "High Speed Multi-Stage Fuzzy Logic Inference and Its Digital Implementation." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/17139257459533024828.
Full textAbstract:
博士<br>國立臺灣大學<br>電機工程學系<br>85<br>In general multi-stage fuzzy logic inference, there are three
kinds of linguistic variables: input, output, and intermediate
variables.When intermediate variables are present in a set of
fuzzy rules to form multi-stage fuzzy rules, the inferring time
involved is understandably lengthened.To achieve fast
reasoning of multi-stage fuzzy logic, the number of fuzzy set
operations of input, output, and intermediate variables must be
decreased as much as possible without information loss.For the
intermediate variables, the fuzzy logic operations of overlapped
fuzzy value pairs on several multi-stage fuzzy rules may be pre-
computed at compiling time.Furthermore, if the pre-computations
have been done, the numbers of intersection and union scalar
operations must also be decreased in order to speed up
reasoning at execution time.Moreover, whether fuzzy logic
operations of multi-stage fuzzy rules are pre-computed or not,
the types of fuzzy logic operators must be considered.Failing to
do so may lead to information loss error.Basing on the above
considerations, we developed a new fast and effective MFLI
algorithm that is supported with rigorous formalism.In
addition, based on the algorithm we propose a novel efficient
hardware architecture and associated digital circuits that can
meet the requirements of complex, compact, portable, and real-
time applications of fuzzy logic.Demonstrated by using the two-
trailer-and-truck backer-parking systems as an example,
significant improvements over existing approaches are attained.
The computational complexity of pre-computations and run-time
scalar operations for the intermediate variable(s) is O(fv),
where fv is the number of fuzzy values for the intermediate
variable(s), which is much lower than that of other existing
MFLI approaches, O(Rule**2).Moreover, for the same example
system with 294 fuzzy rules (245 rules in the first stage and
49 rules in the second stage), simulated with Verilog
language, the novel MFLI hardware architecture with pre-
computation technique takes 128 clocks in an inference cycle
of the worst case; and the novel MFLI hardware architecture
takes 304 clocks with defuzzification for the intermediate
variable phi_i_1.
49
Wagner, Gerd. "A logical reconstruction of fuzzy inference in databases and logic programs." 1997. https://ul.qucosa.de/id/qucosa%3A31962.
Full textAbstract:
We propose to replace Zadeh's DeMorgan-type negation in fuzzy logic by a Heyting-type negation which, unlike the former, preserves the law of the excluded contradiction and is more in line with negation in databases and logic programs. We show that the resulting system can be used for obtaining conservative extensions of relational and deductive databases (resp. normal logic programs).
50
Yi-Zheng, Chen. "Fuzzy-Logic-Based Guidance System Design." 2000. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0009-0112200611313302.
Full text