Dissertations / Theses on the topic 'Fuzzy computation'
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Galea, Michelle. "Fuzzy rules from ant-inspired computation." Thesis, University of Edinburgh, 2007. http://hdl.handle.net/1842/2701.
Full textHaas, Benjamin D. "Efficient general type-2 fuzzy computation." abstract and full text PDF (UNR users only), 2009. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1464436.
Full textAMARAL, JOSE FRANCO MACHADO DO. "SYNTHESIS OF FUZZY SYSTEMS THROUGH EVOLUTIONARY COMPUTATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2003. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=3550@1.
Full textSíntese de Sistemas Fuzzy por Computação Evolucionária propõe uma metodologia de projeto para o desenvolvimento de sistemas fuzzy fundamentada em técnicas de computação evolucionária. Esta metodologia contempla as etapas de concepção do sistema fuzzy e a implementação em hardware do circuito eletrônico que o representa. A concepção do sistema é realizada num ambiente de projeto no qual sua base de conhecimento - composta da base de regras e demais parâmetros característicos - é evoluída, por intermédio de simulação, através do emprego de um novo algoritmo de três estágios que utiliza Algoritmos Genéticos. Esta estratégia enfatiza a interpretabilidade e torna a criação do sistema fuzzy mais simples e eficiente para o projetista, especialmente quando comparada com o tradicional ajuste por tentativa e erro. A implementação em hardware do circuito é realizada em plataforma de desenvolvimento baseada em Eletrônica Evolucionária. Um conjunto de circuitos, denominados de blocos funcionais, foi desenvolvido e evoluído com sucesso para viabilizar a construção da estrutura final do sistema fuzzy.
Synthesis of Fuzzy Systems through Evolutionary Computation proposes a methodology for the design of fuzzy systems based on evolutionary computation techniques. A three-stage evolutionary algorithm that uses Genetic Algorithms (GAs) evolves the knowledge base of a fuzzy system - rule base and parameters. The evolutionary aspect makes the design simpler and more efficient, especially when compared with traditional trial and error methods. The method emphasizes interpretability so that the resulting strategy is clearly stated. An Evolvable Hardware (EHW) platform for the synthesis of analog electronic circuits is proposed. This platform, which can be used for the implementation of the designed fuzzy system, is based on a Field Programmable Analog Array (FPAA). A set of evolved circuits called functional blocks allows the implementation of the fuzzy system.
Bush, Brian O. "Development of a fuzzy system design strategy using evolutionary computation." Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1178656308.
Full textCreaser, Paul. "Application of evolutionary computation techniques to missile guidance." Thesis, Cranfield University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367124.
Full textMatthews, Stephen. "Learning lost temporal fuzzy association rules." Thesis, De Montfort University, 2012. http://hdl.handle.net/2086/8257.
Full textBondugula, Rajkumar. "A novel framework for protein structure prediction." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4855.
Full textThe entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on March 23, 2009) Vita. Includes bibliographical references.
Silva, Ricardo Coelho. "Programação multi-objetivo fuzzy." [s.n.], 2009. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260594.
Full textTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
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Resumo: O objetivo deste trabalho é buscar, estudar e estabelecer as condições de otimali-dade para resolver problemas de programação multi-objetivo irrestritos e restritos em um ambiente impreciso. Essas imprecisões estão presentes nos problemas da vida real e existem muitas formas de tratá-las, mas nesse trabalho será usado a teoria de conjuntos nebulosos. Utilizando como base a otimização nebulosa, foram desenvolvidas duas abordagens para resolver problemas multi-objetivo nebulosos. A primeira abordagem transforma um problema nebuloso em um problema clássico paramétrico com um número maior de funções objetivo, a qual é chamada de paramétrica. A segunda abordagem, chamada de possibilística, usa a teoria de possibilidade como um índice de comparação entre números nebulosos com a finalidade de garantir condições de otimalidade em um ambiente nebuloso. Alguns exemplos numéricos são resolvidos usando um algoritmo genético chamado NSGA-II elitista, com algumas modificações para a comparação de números nebulosos, e depois feita uma análise dos resultados encontrados por ambos os enfoques.
Abstract: The main goal of this work is to search, study and present the optimality conditions to solve the unconstraint and constraint multiobjetive programming problems in imprecise environment. These imprécisions can be found in the real-world optimization problems and there are utmost ways for dealing with them, but in this work will be used the theory of fuzzy sets. Using as a basis the fuzzy optimization, two approaches were developed to solve fuzzy multiobjective problems. The first approach transforms a fuzzy problem into a parametric classic multiobjective programming problem with many more objective functions, which is called parametric approach. The second one, called possibilistic, uses the possibility theory as a comparison index between two fuzzy numbers in order to ensure optimality conditions in a fuzzy environment. Some numerical examples are solved by using a genetic algorithm called elitist NSGA-II with some modifications to compare fuzzy numbers, and then the results obtained with both approaches are analysed.
Doutorado
Automação
Doutor em Engenharia Elétrica
Leite, Leandro da Costa Moraes. "Geração e Simplificação da Base de Conhecimento de um Sistema Híbrido Fuzzy-Genético." Universidade do Estado do Rio de Janeiro, 2009. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=7530.
Full textGenetic-Fuzzy Systems Generation and Simplification of a Knowledge Base proposes a methodology to develop a knowledge base for fuzzy systems through the utilization of evolutionary computational techniques. The evolved fuzzy systems are evaluated considering two distinct criteria: performance and interpretability. Another Fuzzy Logic-based methodology for multiobjective problem analysis was also developed in this work and incorporated in GAs fitness evaluation process. The aforementioned systems were analyzed through computational simulations, and the results were compared to those obtained through other methods, in some applications. The proposed methodology demonstrated that the evolved fuzzy systems are capable of not only good performance, but also good interpretation of their knowledge base, thus showing that they can be effectively used in real world projects.
[Verfasser], Habtamu Gezahegn Tolossa, and Silke [Akademischer Betreuer] Wieprecht. "Sediment transport computation using a data-driven adaptive neuro-fuzzy modelling approach / Habtamu Gezahegn Tolossa. Betreuer: Silke Wieprecht." Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2012. http://d-nb.info/1024692574/34.
Full textBurdelis, Mauricio Alexandre Parente. "Ajuste de taxas de mutação e de cruzamento de algoritmos genéticos utilizando-se inferências nebulosas." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-14082009-180444/.
Full textThis work addressed a proposal of the application of Fuzzy Systems to adjust parameters of Genetic Algorithms, during execution time. This application attempts to improve the performance of Genetic Algorithms by diminishing, at the same time: the average number of necessary generations for a Genetic Algorithm to find the desired global optimum value, as well as diminishing the number of executions of a Genetic Algorithm that are not capable of finding the desired global optimum value even for high numbers of generations. For that purpose, the results of many experiments with Genetic Algorithms were analyzed; addressing instances of the Function Minimization and the Travelling Salesman problems, under different parameter configurations. With the results obtained from these experiments, a model was proposed, for the exchange of parameter values of Genetic Algorithms, in execution time, by using Fuzzy Systems, in order to improve the performance of the system, minimizing both of the measures previously cited.
Abraham, Ajith 1968. "Hybrid soft computing : architecture optimization and applications." Monash University, Gippsland School of Computing and Information Technology, 2002. http://arrow.monash.edu.au/hdl/1959.1/8676.
Full textAmaral, Wanessa Machado do. "Teoria de jogos nebulosos na resolução de problemas de decisão e conflito de interesses." [s.n.], 2007. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259057.
Full textDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
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Resumo: A teoria de jogos é um ramo da teoria da decisão que modela e trata matematicamente situações de conflito de interesses entre entidades, onde o objetivo principal é escolher a melhor estratégia para cada uma delas, ou seja, aquela que se traduz em equilíbrio. Existem inúmeras áreas em que a teoria de jogos é utilizada. Uma das principais é a microeconomia, onde se aborda questões relativas ao comportamento de empresas e indústrias no mercado competitivo. A teoria de jogos é utilizada para encontrar a estratégia ótima para empresas com objetivos antagônicos, como exige o mercado. No entanto, os dados dos problemas reais nem sempre são precisos. A teoria de conjuntos nebulosos introduz flexibilidade na formulação desses problemas, pois permite a consideração de parâmetros imprecisos nos modelos. Esse trabalho aborda a teoria de jogos nebulosos. Estratégias de equilíbrio são analisadas e métodos computacionais desenvolvidos para a resolução dos modelos. É proposto um método baseado em computação evolutiva para obter soluções de equilíbrio de jogos nebulosos. Além disso propõe-se também um método baseado em a-cortes e no algoritmo de decomposição para a solução dos modelos bilineares associados a jogos nebulosos de soma não zero. Exemplos de aplicações são apresentados para ilustrar o potencial prático da teoria de jogos nebulosos
Abstract: Game theory is a branch of applied mathematics whose aim is to model and study decision making in conflicting situations. In these situations, the main goal is to choose the best strategy for all the players in the game, that is, to find the equilibrium solutions. Game theory can be defined as the study of how self-interested entities interact and make decisions. There are many applications of game theory in different areas. One of the main applications is in microeconomy, where situations of conflict between companies exist and there is a need to find the optimal strategies in that situation. In practice however, model parameters are imprecise. Fuzzy set theory allows modeling flexibility because imprecise data can be treated using fuzzy models. This work concerns Fuzzy Game Theory. Equilibrium strategies are studied and computational methods developed to solve fuzzy game problems. A new method to solve fuzzy games using evolutionary computation is introduced. A method based on a-cuts and on a decomposition algorithm to solve bilinear models also presented to solve fuzzy non zero-sum games. Algorithms were implemented and applications examples are discussed to illustrate the usefulness of fuzzy games in practice
Mestrado
Engenharia de Computação
Mestre em Engenharia Elétrica
Almeida, Tiago Agostinho de. "Computação evolutiva aplicada a resolução do problema da arvore geradora minima com parametros fuzzy." [s.n.], 2006. http://repositorio.unicamp.br/jspui/handle/REPOSIP/261704.
Full textDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
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Resumo: Este trabalho propoe meta-heuristicas baseadas em tecnicas da computaçao evolutiva, que visam encontrar um conjunto de arvores geradoras minimas para problemas de grafos, que possuem incertezas em relaçao as informaçoes associadas aos parametros. Resolver problemas dessa natureza e um processo NP-Completo, pois envolve um numero enorme de comparaçoes. A fim de contornar essa complexidade, este trabalho propoe um algoritmo genetico e um sistema imunologico artificial, capazes de explorar eficientemente o espaco de busca e de obter resultados satisfatorios, sem a necessidade de confrontar todas as solucoes entre si
Abstract: This work proposes heuristical approaches based on evolutionary computation, whose goal is to find a set of minimum spanning trees in graphs that contain uncertainties in their parameters. These kind of problems is a NP-hard one, because it involves an enormous number of comparisons. In order to avoid this complexity, this work proposes a genetic algorithm and an artificial immune system, that explore efficiently the search space of solutions to looking for satisfactory results, without the necessity of comparing all possible solutions. Keywords: Fuzzy Graph, Fuzzy Minimum Spanning Tree, Fuzzy Set Theory, Evolutionary Computation, Genetic Algorithm, Artificial Immune System
Mestrado
Telecomunicações e Telemática
Mestre em Engenharia Elétrica
Rosa, Raul Arthur Fernandes 1989. "Redes neurais evolutivas com aprendizado extremo recursivo." [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259065.
Full textDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
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Resumo: Esta dissertação estuda uma classe de redes neurais evolutivas para modelagem de sistemas a partir de um fluxo de dados. Esta classe é caracterizada por redes evolutivas com estruturas feedforward e uma camada intermediária cujo número de neurônios é variável e determinado durante a modelagem. A aprendizagem consiste em utilizar métodos de agrupamento para estimar o número de neurônios na camada intermediária e algoritmos de aprendizagem extrema para determinar os pesos da camada intermediária e de saída da rede. Neste caso, as redes neurais são chamadas de redes neurais evolutivas. Um caso particular de redes evolutivas é quando o número de neurônios da camada intermediária é determinado a priori, mantido fixo, e somente os pesos da camada intermediária e de saída da rede são atualizados de acordo com dados de entrada. Os algoritmos de agrupamento e de aprendizagem extrema que compõem os métodos evolutivos são recursivos, pois a aprendizagem ocorre de acordo com o processamento de um fluxo de dados. Em particular, duas redes neurais evolutivas são propostas neste trabalho. A primeira é uma rede neural nebulosa híbrida evolutiva. Os neurônios da camada intermediária desta rede são unineurônios, neurônios nebulosos com processamento sináptico realizado por uninormas. Os neurônios da camada de saída são sigmoidais. Um algoritmo recursivo de agrupamento baseado em densidade, chamado de nuvem, é utilizado para particionar o espaço de entrada-saída do sistema e estimar o número de neurônios da camada intermediária da rede; a cada nuvem corresponde um neurônio. Os pesos da rede neural nebulosa híbrida são determinados utilizando a máquina de aprendizado extremo com o algoritmo quadrados mínimos recursivo ponderado. O segundo tipo de rede proposto neste trabalho é uma rede neural multicamada evolutiva com neurônios sigmoidais na camada intermediária e de saída. Similarmente à rede híbrida, nuvens particionam o espaço de entrada-saída do sistema e são utilizadas para estimar o número de neurônios da camada intermediária. O algoritmo para determinar os pesos da rede é a mesma versão recursiva da máquina de aprendizado extremo. Além das redes neurais evolutivas, sugere-se também uma variação da rede adaptativa OS-ELM (online sequential extreme learning machine) mantendo o número de neurônios na camada intermediária fixo e introduzindo neurônios sigmoidais na camada de saída. Neste caso, a aprendizagem usa o algoritmo dos quadrados mínimos recursivo ponderado no aprendizado extremo. As redes foram analisadas utilizando dois benchmarks clássicos: identificação de forno a gás com o conjunto de dados de Box-Jenkins e previsão de série temporal caótica de Mackey-Glass. Dados sintéticos foram gerados para analisar as redes neurais na modelagem de sistemas com parâmetros e estrutura variantes no tempo (concept drif e concept shift). Os desempenhos foram quantificados usando a raiz quadrada do erro quadrado médio e avaliados com o teste estatístico de Deibold-Mariano. Os desempenhos das redes neurais evolutivas e da rede adaptativa foram comparados com os desempenhos da rede neural com aprendizagem extrema e dos métodos de modelagem evolutivos representativos do estado da arte. Os resultados mostram que as redes neurais evolutivas sugeridas neste trabalho são competitivas e têm desempenhos similares ou superiores às abordagens evolutivas propostas na literatura
Abstract: Abstract: This dissertation studies a class of evolving neural networks for system modeling from data streams. The class encompasses single hidden layer feedforward neural networks with variable and online de nition of the number of hidden neurons. Evolving neural network learning uses clustering methods to estimate the number of hidden neurons simultaneously with extreme learning algorithms to compute the weights of the hidden and output layers. A particular case is when the evolving network keeps the number of hidden neurons xed. In this case, the number of hidden neurons is found a priori, and the hidden and output layer weights updated as data are input. Clustering and extreme learning algorithms are recursive. Therefore, the learning process may occur online or real-time using data stream as input. Two evolving neural networks are suggested in this dissertation. The rst is na evolving hybrid fuzzy neural network with unineurons in the hidden layer. Unineurons are fuzzy neurons whose synaptic processing is performed using uninorms. The output neurons are sigmoidals. A recursive clustering algorithm based on density and data clouds is used to granulate the input-output space, and to estimate the number of hidden neurons of the network. Each cloud corresponds to a hidden neuron. The weights of the hybrid fuzzy neural network are found using the extreme learning machine and the weighted recursive least squares algorithm. The second network is an evolving multilayer neural network with sigmoidal hidden and output neurons. Like the hybrid neural fuzzy network, clouds granulate the input-output space and gives the number of hidden neurons. The algorithm to compute the network weights is the same recursive version of the extreme learning machine. A variation of the adaptive OS-ELM (online sequential extreme learning machine) network is also suggested. Similarly as the original, the new OS-ELM xes the number of hidden neurons, but uses sigmoidal instead of linear neurons in the output layer. The new OS-ELM also uses weighted recursive least square.The hybrid and neural networks were evaluated using two classic benchmarks: the gas furnace identi cation using the Box-Jenkins data, and forecasting of the chaotic Mackey-Glass time series. Synthetic data were produced to evaluate the neural networks when modeling systems with concept drift and concept shift. This a modeling circumstance in which system structure and parameters change simultaneously. Evaluation was done using the root mean square error and the Deibold-Mariano statistical test. The performance of the evolving and adaptive neural networks was compared against neural network with extreme learning, and evolving modeling methods representative of the current state of the art. The results show that the evolving neural networks and the adaptive network suggested in this dissertation are competitive and have similar or superior performance than the evolving approaches proposed in the literature
Mestrado
Engenharia de Computação
Mestre em Engenharia Elétrica
Farias, Weslley Alves. "Comparação entre controladores fuzzy e neural desenvolvidos via simulação e transferidos para ambientes reais no âmbito da robótica evolutiva." Pós-Graduação em Engenharia Elétrica, 2018. http://ri.ufs.br/jspui/handle/riufs/9569.
Full textOne of the greatest limitations of Evolutionary Robotics is when transfering controllers evolved by simulation to real environments. This limitation is mainly caused by model simplifications and difficulties to represent dynamic characteristics, whether from the robot or the environment. And this results in performance degradation of the evolved controller after the transfer, a phenomenon called reality gap. Because this problem is a limitation for practical and complex applications of evolutionary robotics, many solutions have been proposed since the 90s. Until now, most of the research use control strategies based on artificial neural networks because they allow algorithms to be evolved with less designer influence. On the other hand, fuzzy logic can also be used for the development of controllers in the field of evolutionary robotics because it also allows emulating human intelligence. Therefore, this dissertation investigates whether fuzzy control systems are more robust than neural control systems, both optimized by a genetic algorithm in simulation and later transferred to a real robot in physical environment in the task of autonomous navigation while avoiding obstacles. The results show that in the analyzed conditions, fuzzy controllers present better transfer characteristics, mainly considering the smoothness of the executed trajectory, and an equivalent performance, when compared with neural controllers.
Uma das grandes limitações da Robótica Evolutiva diz respeito à transferência de controladores evoluídos por simulação e transferidos ao ambiente real. Tal limitação devese, sobretudo, a simplificações de modelo e dificuldades na representação de características dinâmicas, tanto do robô quanto do ambiente, e isso resulta na queda de desempenho do controlador evoluído após a transferência, fenômeno denominado de reality gap. Muitas soluções vêm sendo propostas desde a década de 90, em virtude deste problema ser uma limitação para aplicações práticas e complexas da robótica evolutiva. Até o momento, a maioria dos trabalhos de pesquisa desenvolvidos utiliza estratégias de controle baseadas em redes neurais artificiais por permitirem que algoritmos possam ser evoluídos com menor influência do projetista. Por outro lado, a lógica fuzzy também pode ser usada para o desenvolvimento de controladores no âmbito da robótica evolutiva, pois também permite emular a inteligência humana. Portanto, nesta dissertação é investigado se sistemas de controle fuzzy são mais robustos que sistemas de controle neurais, ambos otimizados por um algoritmo genético em simulação e posteriormente transferidos para um robô real em ambiente físico na tarefa de navegação autônoma evitando obstáculos. Como resultado, obteve-se que nas condições analisadas, os controladores fuzzy apresentaram uma melhor transferência, com destaque para a suavidade da trajetória executada, e um desempenho equivalente, quando comparados com controladores neurais.
São Cristóvão, SE
Yusuf, Syed Adnan. "An evolutionary AI-based decision support system for urban regeneration planning." Thesis, University of Wolverhampton, 2010. http://hdl.handle.net/2436/114896.
Full textRashid, Kashif. "Optimisation in electromagnetics using computational intelligence." Thesis, Imperial College London, 2000. http://hdl.handle.net/10044/1/8735.
Full textMaciel, Leandro dos Santos 1986. "Estimação e previsão da estrutura a termo das taxas de juros usando técnicas de inteligência computacional." [s.n.], 2012. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260710.
Full textDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
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Resumo: Este trabalho propõe a utilização de técnicas de inteligência computacional para a estimação e previsão da estrutura a termo das taxas de juros, com base em dados dos mercados de renda fixa dos Estados Unidos e Brasil. Para o problema de estimação da curva de juros, as técnicas de computação evolucionária, Algoritmos Genéticos, Evolução Diferencial e Estratégias Evolutivas, foram comparadas com abordagens tradicionais da literatura, como mínimos quadrados não-lineares e programação quadrática sequencial. A motivação da aplicação de técnicas de computação evolucionária no problema de estimação da estrutura a termo busca superar limitações como não-convergência e elevada instabilidade dos parâmetros à inicialização. Além disso, recentemente, a literatura tem apontado o elevado desempenho dos algoritmos genéticos em problemas de modelagem da curva de rendimentos. Outra contribuição deste trabalho consiste no desenvolvimento de um modelo nebuloso evolutivo de aprendizado participativo estendido, denominado ePL+, que inclui em sua versão original, ePL, mecanismos para aumentar sua autonomia e adaptabilidade na modelagem de sistemas complexos. Dessa forma, o modelo ePL+ e outros modelos nebulosos funcionais evolutivos foram avaliados na questão da previsão das taxas futuras de juros, em contraposição com modelos econométricos baseados em processos autoregressivos e modelos de redes neurais artificiais multi-camadas, uma vez que a evolução das taxas de juros apresenta uma dinâmica altamente não-linear e variante no tempo, justificando a ideia de modelagem adaptativa. O desempenho dos métodos considerados foi avaliado em termos de métricas de erro, complexidade computacional e por meio de testes estatísticos paramétricos e não-paramétricos, MGN e SIGN, respectivamente. Os resultados evidenciaram o elevado potencial dos modelos de inteligência computacional na estimação e previsão da estrutura a termo em ambas economias consideradas, constatado pelo melhor desempenho, em termos de ajuste e significância estatística, em relação às técnicas de otimização de parâmetros e econométricas mais utilizadas na literatura
Abstract: This work proposes the term structure of interest rates modeling and forecasting using computational intelligence techniques, based on data from the US and Brazilian fixed income markets. The yield curve modeling includes the use of some evolutionary computation methods like Genetic Algorithms, Differential Evolution and Evolution Strategies in comparison with traditional optimization techniques such as nonlinear least squares and sequential quadratic programming. The motivation behind the use of evolutionary computation to yield curve estimation aims to overcome limitations like non-convergence and high parameters instability to initialization. Moreover, recently, the literature has been shown the higher performance of genetic algorithms in yield curve modeling problems. This work also contributes by developing an extended participatory learning fuzzy model, called ePL+, which includes on its original version, ePL, mechanisms to improve its autonomy and adaptability in complex systems modeling. Therefore, the ePL+ model and some evolving functional fuzzy approaches were evaluated in the future interest rates forecasting, as opposed to econometric models based on autoregressive processes and multilayer artificial neural networks methodologies, since interest rates evolution shows a high non-linear dynamics and also time-varying, justifying the idea of adaptive modeling. Models' performance were compared in terms of error measures, computational complexity and by parametric and non-parametric statistical tests, MGN and SIGN, respectively. The results reveal the high potential of computational intelligence methods to deal with the term structure modeling and forecasting for both economies considered, as pointed out by their adjustment and statistical superior performance then traditional optimization and econometrics techniques reported in the literature
Mestrado
Automação
Mestre em Engenharia Elétrica
Remias, Michael George. "Computational studies of some fuzzy mathematical problems." Thesis, Curtin University, 2012. http://hdl.handle.net/20.500.11937/1147.
Full textClaessen, Mark Johan Alexander. "A soft-computational theory of conceptual categorization." Thesis, University of Exeter, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302544.
Full textKim, Hantae. "FUZZY RULE-BASED RELAXATION SCHEMES IN GROUNDWATER FLOW COMPUTATIONS." Kyoto University, 1998. http://hdl.handle.net/2433/182424.
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新制・課程博士
博士(農学)
甲第7563号
農博第1024号
新制||農||772(附属図書館)
学位論文||H10||N3213(農学部図書室)
UT51-99-A249
京都大学大学院農学研究科農業工学専攻
(主査)教授 河地 利彦, 教授 青山 咸康, 教授 三野 徹
学位規則第4条第1項該当
Kotta, Anwesh. "Condition Monitoring : Using Computational intelligence methods." Master's thesis, Universitätsbibliothek Chemnitz, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-187100.
Full textChuang, Wei Kuo. "Computational model for engineering design and development." Thesis, Brunel University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263496.
Full textSmit, Marius. "Interactive narrative generation using computational verb theory." Diss., University of Pretoria, 2009. http://hdl.handle.net/2263/27510.
Full textDissertation (MSc)--University of Pretoria, 2010.
Computer Science
Unrestricted
RICARTE, MORENO LUIS-ALBERTO. "Topological and Computational Models for Fuzzy Metric Spaces via Domain Theory." Doctoral thesis, Universitat Politècnica de València, 2013. http://hdl.handle.net/10251/34670.
Full textRicarte Moreno, L. (2013). Topological and Computational Models for Fuzzy Metric Spaces via Domain Theory [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/34670
TESIS
Che, Fidelis Ndeh. "Object-oriented analysis and design of computational intelligence systems." Thesis, City University London, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.245861.
Full textExner, Thomas Eckart. "Computergestützte Strukturbestimmung biochemischer Komplexe durch einen Fuzzy Logic-basierten Algorithmus." [S.l.] : [s.n.], 2000. http://deposit.ddb.de/cgi-bin/dokserv?idn=960419330.
Full textMiranda, Luís Miguel Gonçalves. "Data fusion with computational intelligence techniques: a case study of fuzzy inference for terrain assessment." Master's thesis, Faculdade de Ciências e Tecnologia, 2014. http://hdl.handle.net/10362/12338.
Full textWith the constant technology progression is inherent storage of all kinds of data. Satellites, mobile phones, cameras and other type of electronic equipment, produce on daily basis an amount of data of gigantic proportions. These data alone may not convey any meaning and may even be impossible to interpret them without specific auxiliary measures. Data fusion contributes in this issue giving use of these data, processing them into proper knowledge for whom analyzes. Within data fusion there are numerous processing approaches and methodologies, being given here highlight to the one that most resembles to the imprecise human knowledge, the fuzzy reasoning. These method is applied in several areas, inclusively as inference system for hazard detection and avoidance in unmanned space missions. To this is fundamental the use of fuzzy inference systems, where the problem is modeled through a set of linguistic rules, fuzzy sets, membership functions and other information. In this thesis it was developed a fuzzy inference system, for safe landing sites using fusion of maps, and a data visualization tool. Thus, classification and validation of the information are made easier with such tools.
Khuman, Arjab Singh. "The quantification of perception based uncertainty using R-fuzzy sets and grey analysis." Thesis, De Montfort University, 2016. http://hdl.handle.net/2086/14225.
Full textConroy, Justin Anderson. "Analysis of adaptive neuro-fuzzy network structures." Thesis, Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/19684.
Full textAuephanwiriyakul, Sansanee. "A study of linguistic pattern recognition and sensor fusion /." free to MU campus, to others for purchase, 2000. http://wwwlib.umi.com/cr/mo/fullcit?p9999270.
Full textCruz, Anderson Paiva. "L?gica BDI fuzzy." Universidade Federal do Rio Grande do Norte, 2008. http://repositorio.ufrn.br:8080/jspui/handle/123456789/17995.
Full textCoordena??o de Aperfei?oamento de Pessoal de N?vel Superior
Intendding to understand how the human mind operates, some philosophers and psycologists began to study about rationality. Theories were built from those studies and nowadays that interest have been extended to many other areas such as computing engineering and computing science, but with a minimal distinction at its goal: to understand the mind operational proccess and apply it on agents modelling to become possible the implementation (of softwares or hardwares) with the agent-oriented paradigm where agents are able to deliberate their own plans of actions. In computing science, the sub-area of multiagents systems has progressed using several works concerning artificial intelligence, computational logic, distributed systems, games theory and even philosophy and psycology. This present work hopes to show how it can be get a logical formalisation extention of a rational agents architecture model called BDI (based in a philosophic Bratman s Theory) in which agents are capable to deliberate actions from its beliefs, desires and intentions. The formalisation of this model is called BDI logic and it is a modal logic (in general it is a branching time logic) with three access relations: B, D and I. And here, it will show two possible extentions that tranform BDI logic in a modal-fuzzy logic where the formulae and the access relations can be evaluated by values from the interval [0,1]
Com o intuito de entender como a mente humana funciona iniciaram-se estudos sobre cogni??o nos campos da filosofia e psicologia. Teorias surgiram desses estudos e, atualmente, esta curiosidade foi estendida a outras ?reas, tais como, ci?ncia e engenharia de computa??o, no entanto, nestas ?reas, o objetivo ? sutilmente diferente: entender o funcionamento da mente e aplic?-lo em uma modelagem artificial. Em ci?ncia da computa??o, a sub-?rea de sistemas multiagentes tem progredido bastante, utilizando trabalhos em intelig?ncia artificial, l?gica computacional, sistemas distribu?dos, teoria dos jogos e, aproveitando tamb?m teorias provenientes da pr?pria filosofia e psicologia. Desta forma, alguns pesquisadores j? v?em o paradigma de programa??o orientado a agentes como a melhor solu??o para a implementa??o dos softwares mais complexos: cujos sistemas s?o din?micos, n?o-determin?sticos e que podem ter de operar com dados faltosos sobre ambientes tamb?m din?micos e n?o-determin?sticos. Este trabalho busca a apresenta??o de uma extens?o da formaliza??o l?gica de um modelo de arquitetura de agentes cognitivos, chamado BDI (belief-desire-intention), na qual o agente ? capaz de deliberar suas a??es baseando-se em suas cren?as, desejos e inten??es. A formaliza??o de tal modelo ? conhecida pelo nome de l?gica BDI, uma l?gica modal com tr?s rela??es de modalidade. Neste trabalho, ser?o apresentados dois planos para transform?-la numa l?gica modal fuzzy onde as rela??es de acessibilidade e as f?rmulas (modais-fuzzy) poder?o ter valora??es dentro do intervalo [0,1]. Esta l?gica modal fuzzy h? de ser um sistema l?gico formal capaz de representar quantitativamente os diferentes graus de cren?as, desejos e inten??es objetivando a constru??o de racioc?nios fuzzy e a delibera??o de a??es de um agente (ou grupo de agentes), atrav?s dessas atitudes mentais (seguindo assim um modelo intensional)
Abu, Abed Wassim [Verfasser]. "Computational Fuzzy Processing of Uncertainty in Environmental Modelling and Simulation / Wassim Abu Abed." Aachen : Shaker, 2010. http://d-nb.info/1084535653/34.
Full textAbdallah, Mohamed E. S. M. "A Novel Computational Approach for the Management of Bioreactor Landfills." Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/20314.
Full textChen, Xiujuan. "Computational Intelligence Based Classifier Fusion Models for Biomedical Classification Applications." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_diss/26.
Full textCosta, Herbert Rodrigues do Nascimento. "Aplicação de técnicas de inteligência artificial em processos de fabricação de vidro." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/3/3139/tde-09032007-171929/.
Full textThe Artificial Intelligence now is a vast research field. There are several techniques exist being researched. In this thesis Fuzzy Theory, Decision Trees and Neural Networks were used. The three techniques have been successfully applied in several applications in the areas of automation and control, pattern recognition, voice recognition, detection of flaws and classification, among others. The Fuzzy Theory allows to work with the uncertainties and they provide a symbolic understanding for understanding of the knowledge. The Decision Trees have capacity to build symbolic decisions for the classification of problems and through the knowledge obtained by the tree could be built symbolic rules for a socket of decision. The Fuzzy Theory can also be incorporate them tree of decision increasing the representation power and applicability of the Decision trees. Neural Networks (algorithm back-propagation) it has been presenting great results in the learning of functions and in classification problems. The contribution of this thesis is to show the application of the three techniques of Artificial Intelligence (AI) in processes of production of Glass. The processes of production of the glass were analyzed and the proposal of the thesis is the application of the techniques of AI in the factories of production of glasses to packings and plane glasses. In the first factory it is applied the techniques of AI to classify the defects that happen in the Glass for Packings in function of the operational conditions of the coalition ovens. In the second factory it is applied the techniques to classify the defects in the matters cousins\' function used in the production of the glass. In the third factory the techniques are applied in the classification of the patterns of production of the plane glass. The results obtained with the classification of defects and patterns were in a satisfactory general way. The three techniques of AI presented were used for the analysis of the bases of data in the three glass factories studied in thesis. The techniques of AI obtained a satisfactory classification for the defects of the glass for packings and for the patterns of the plane glasses. The results obtained starting from the techniques are compared and they present promising results.
Hilmer, Tanja. "Water in society integrated optimisation of sewerage systems and wastewater treatment plants with computational intelligence tools." Berlin mbv, 2008. http://d-nb.info/990627608/04.
Full textFunsten, Brad Thomas Mr. "ECG Classification with an Adaptive Neuro-Fuzzy Inference System." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1380.
Full textAbel, Andrew. "Towards an intelligent fuzzy based multimodal two stage speech enhancement system." Thesis, University of Stirling, 2013. http://hdl.handle.net/1893/15989.
Full textZarei, Anahita. "A novel assessment index and intelligent predictive models for orthodontics /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/6093.
Full textPereira, Claudio Robinson Tapié. "Sistema de tomada de decisão para compra e venda de ativos financeiros utilizando lógica fuzzy." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-06112008-104532/.
Full textThe Proteu Fuzzy System is a decision-making system with the purpose of supporting a technical analyst issuing (impartial and rational) buy and sell signals for a financial asset. The system use, for the decision-making process, an inference engine based on Fuzzy Logic and technical indicators (e.g. Moving Averages, MACD and RSI). The simulation shows that the system is able to generate profits in a consistent manner and with a lower volatility then the market for some assets.
Furno, Domenico. "Hybrid approaches based on computational intelligence and semantic web for distributed situation and context awareness." Doctoral thesis, Universita degli studi di Salerno, 2013. http://hdl.handle.net/10556/927.
Full textThe research work focuses on Situation Awareness and Context Awareness topics. Specifically, Situation Awareness involves being aware of what is happening in the vicinity to understand how information, events, and one’s own actions will impact goals and objectives, both immediately and in the near future. Thus, Situation Awareness is especially important in application domains where the information flow can be quite high and poor decisions making may lead to serious consequences. On the other hand Context Awareness is considered a process to support user applications to adapt interfaces, tailor the set of application-relevant data, increase the precision of information retrieval, discover services, make the user interaction implicit, or build smart environments. Despite being slightly different, Situation and Context Awareness involve common problems such as: the lack of a support for the acquisition and aggregation of dynamic environmental information from the field (i.e. sensors, cameras, etc.); the lack of formal approaches to knowledge representation (i.e. contexts, concepts, relations, situations, etc.) and processing (reasoning, classification, retrieval, discovery, etc.); the lack of automated and distributed systems, with considerable computing power, to support the reasoning on a huge quantity of knowledge, extracted by sensor data. So, the thesis researches new approaches for distributed Context and Situation Awareness and proposes to apply them in order to achieve some related research objectives such as knowledge representation, semantic reasoning, pattern recognition and information retrieval. The research work starts from the study and analysis of state of art in terms of techniques, technologies, tools and systems to support Context/Situation Awareness. The main aim is to develop a new contribution in this field by integrating techniques deriving from the fields of Semantic Web, Soft Computing and Computational Intelligence. From an architectural point of view, several frameworks are going to be defined according to the multi-agent paradigm. Furthermore, some preliminary experimental results have been obtained in some application domains such as Airport Security, Traffic Management, Smart Grids and Healthcare. Finally, future challenges is going to the following directions: Semantic Modeling of Fuzzy Control, Temporal Issues, Automatically Ontology Elicitation, Extension to other Application Domains and More Experiments. [edited by author]
XI n.s.
Dandignac, Mitchell Edward. "A Computational Linguistic Paradigm for Assessing the Comprehension and Social Diffusion of Medical Information." Miami University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=miami1626091707909761.
Full textSá, Hindenburgo Elvas Gonçalves de. "Um método baseado em inteligência computacional para a geração automática de casos de teste de caixa preta." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-29112010-153615/.
Full textThis dissertation work presents a method based on computational intelligence techniques, such as learning set of rules, artificial neural networks and fuzzy logic, proposed the development of tools that generate test cases and sort of black box with the purposes of assisting activity in the preparation of tests for detection of defects in features or functionality and decreasing the detection time correction software aimed, with this, reach a qualitatively higher test coverage to the manual creation process. The acquisition of new test cases and classification of test cases generated using techniques Learning learning a whole set of Regrasregras using sequential covering algorithms, and a fuzzy inference machine. The definition of methods, both to generate and to classify the test cases were substantiated in experiments aimed at comparing the similarities between the fuzzy methods, neural networks and learning of the rule set. Finally, we sought to develop a tool for evidence of concepts aiming to apply the methods which obtained better results in trials. The criteria adopted to define the methods were metrics cyclomatic complexity and total lines of code (LOC).
He, Yuanchen. "Fuzzy-Granular Based Data Mining for Effective Decision Support in Biomedical Applications." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_diss/12.
Full textLin, Shang-chih, and 林尚志. "Apply Fuzzy Computation to Time-Frequency Analysis." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/91418979635887337627.
Full text大同大學
資訊工程研究所
90
In this thesis, the relation between Fourier or short-time Fourier transform and fuzzy technique in signal processing is studied. Traditionally, the fuzzy technique is used to classifying or other processing after the transform is done. It may be expected that consistent processes would reduced the complexity in implementations. Fourier transform has been widely used in many researches and applications. It converts signals of time domain into spectrum of frequency domain. But somehow, in practical applications, frequency contents of signals often change from time to time. Joint time-frequency analysis is more appreciated in most application. The short-time Fourier transform (STFT) is the most intuitional transforms, but there are some drawbacks with it. The window effects and too much complex number computation are two main drawbacks of STFT. Fuzzy technique has been used in control system for many years and it works well. It is easy and efficient to implement. We want to combine the technique of time-frequency analysis and fuzzy. Increase computational efficiency in time-frequency analysis and reduce the tuning effort of membership function in fuzzy. Fourier transform and short-time Fourier transform are implemented by fuzzy technique to relate the time-frequency analysis and fuzzy technique. And then some new time-frequency analysis implemented by fuzzy technique are proposed.
Wan, Yu-en, and 萬又恩. "Computation on solutions of fuzzy linear systems." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/15633424623149905332.
Full text國立臺南大學
應用數學研究所碩士班
101
We re-synthesize three different methods dealing with fuzzy linear system in this paper, and make deeper introduction to one of the methods refering to the algorithms of the Moore-Penrose inverse. However,there is a counterexample that the so-called weak solution of a fuzzy linear system, defined by Friedman, is not always a fuzzy number vector. We find a way to solve this problem. Finally, we compare the methods,and make a proper induction based on their commonness.
(9863570), J. Zajaczkowski. "Analysis of the hierarchical fuzzy control using evolutionary algorithms." Thesis, 2010. https://figshare.com/articles/thesis/Analysis_of_the_hierarchical_fuzzy_control_using_evolutionary_algorithms/13462052.
Full text(9777542), Mohamed Anver. "Fuzzy algorithms for image enhancement and edge detection." Thesis, 2004. https://figshare.com/articles/thesis/Fuzzy_algorithms_for_image_enhancement_and_edge_detection/13465622.
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