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Galea, Michelle. "Fuzzy rules from ant-inspired computation." Thesis, University of Edinburgh, 2007. http://hdl.handle.net/1842/2701.
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This research identifies and investigates major issues in inducing accurate and comprehensible fuzzy rules from datasets. A review of the current literature on fuzzy rulebase induction uncovers two significant issues: A. There is a tradeoff between inducing accurate fuzzy rules and inducing comprehensible fuzzy rules; and, B. A common strategy for the induction of fuzzy rulebases, that of iterative rule learning where the rules are generated one by one and independently of each other, may not be an optimal one. FRANTIC, a system that provides a framework for exploring the claims above is developed. At the core lies a mechanism for creating individual fuzzy rules. This is based on a significantly modified social insect-inspired heuristic for combinatorial optimisation -- Ant Colony Optimisation. The rule discovery mechanism is utilised in two very different strategies for the induction of a complete fuzzy rulebase: 1. The first follows the common iterative rule learning approach for the induction of crisp and fuzzy rules; 2. The second has been designed during this research explicitly for the induction of a fuzzy rulebase, and generates all rules in parallel. Both strategies have been tested on a number of classification problems, including medical diagnosis and industrial plant fault detection, and compared against other crisp or fuzzy induction algorithms that use more well-established approaches. The results challenge statement A above, by presenting evidence to show that one criterion need not be met at the expense of the other. This research also uncovers the cost that is paid -- that of computational expenditure -- and makes concrete suggestions on how this may be resolved. With regards to statement B, until now little or no evidence has been put forward to support or disprove the claim. The results of this research indicate that definite advantages are offered by the second simultaneous strategy, that are not offered by the iterative one. These benefits include improved accuracy over a wide range of values for several key system parameters. However, both approaches also fare well when compared to other learning algorithms. This latter fact is due to the rule discovery mechanism itself -- the adapted Ant Colony Optimisation algorithm -- which affords several additional advantages. These include a simple mechanism within the rule construction process that enables it to cope with datasets that have an imbalanced distribution between the classes, and another for controlling the amount of fit to the training data. In addition, several system parameters have been designed to be semi-autonomous so as to avoid unnecessary user intervention, and in future work the social insect metaphor may be exploited and extended further to enable it to deal with industrial-strength data mining issues involving large volumes of data, and distributed and/or heterogeneous databases.
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Haas, 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.
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AMARAL, 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.
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UNIVERSIDADE DO ESTADO DO RIO DE JANEIROSíntese 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.
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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.
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Creaser, Paul. "Application of evolutionary computation techniques to missile guidance." Thesis, Cranfield University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367124.
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Matthews, Stephen. "Learning lost temporal fuzzy association rules." Thesis, De Montfort University, 2012. http://hdl.handle.net/2086/8257.
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Fuzzy association rule mining discovers patterns in transactions, such as shopping baskets in a supermarket, or Web page accesses by a visitor to a Web site. Temporal patterns can be present in fuzzy association rules because the underlying process generating the data can be dynamic. However, existing solutions may not discover all interesting patterns because of a previously unrecognised problem that is revealed in this thesis. The contextual meaning of fuzzy association rules changes because of the dynamic feature of data. The static fuzzy representation and traditional search method are inadequate. The Genetic Iterative Temporal Fuzzy Association Rule Mining (GITFARM) framework solves the problem by utilising flexible fuzzy representations from a fuzzy rule-based system (FRBS). The combination of temporal, fuzzy and itemset space was simultaneously searched with a genetic algorithm (GA) to overcome the problem. The framework transforms the dataset to a graph for efficiently searching the dataset. A choice of model in fuzzy representation provides a trade-off in usage between an approximate and descriptive model. A method for verifying the solution to the hypothesised problem was presented. The proposed GA-based solution was compared with a traditional approach that uses an exhaustive search method. It was shown how the GA-based solution discovered rules that the traditional approach did not. This shows that simultaneously searching for rules and membership functions with a GA is a suitable solution for mining temporal fuzzy association rules. So, in practice, more knowledge can be discovered for making well-informed decisions that would otherwise be lost with a traditional approach.
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Bondugula, Rajkumar. "A novel framework for protein structure prediction." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4855.
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Thesis (Ph.D.)--University of Missouri-Columbia, 2007.The 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.
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Silva, Ricardo Coelho. "Programação multi-objetivo fuzzy." [s.n.], 2009. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260594.
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Orientadores: Akebo Yamakami, Jose Luis Verdegay GaldeanoTese (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
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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.
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Geração e Simplificação da Base de Conhecimento de um Sistema Híbrido Fuzzy-
Genético propõe uma metodologia para o desenvolvimento da base de conhecimento de
sistemas fuzzy, fundamentada em técnicas de computação evolucionária. Os sistemas fuzzy
evoluídos são avaliados segundo dois critérios distintos: desempenho e interpretabilidade.
Uma metodologia para a análise de problemas multiobjetivo utilizando a Lógica Fuzzy foi
também desenvolvida para esse fim e incorporada ao processo de avaliação dos AGs. Os
sistemas fuzzy evoluídos foram avaliados através de simulações computacionais e os
resultados obtidos foram comparados com os obtidos por outros métodos em diferentes tipos
de aplicações. O uso da metodologia proposta demonstrou que os sistemas fuzzy evoluídos
possuem um bom desempenho aliado a uma boa interpretabilidade da sua base de
conhecimento, tornando viável a sua utilização no projeto de sistemas reais.Genetic-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.
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[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.
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Burdelis, 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/.
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Neste trabalho foi realizada uma proposta de utilização de Sistemas de Inferência Nebulosos para controlar, em tempo de execução, parâmetros de Algoritmos Genéticos. Esta utilização busca melhorar o desempenho de Algoritmos Genéticos diminuindo, ao mesmo tempo: a média de iterações necessárias para que um Algoritmo Genético encontre o valor ótimo global procurado; bem como diminuindo o número de execuções do mesmo que não são capazes de encontrar o valor ótimo global procurado, nem mesmo para quantidades elevadas de iterações. Para isso, foram analisados os resultados de diversos experimentos com Algoritmos Genéticos, resolvendo instâncias dos problemas de Minimização de Funções e do Caixeiro Viajante, sob diferentes configurações de parâmetros. Com base nos resultados obtidos a partir destes experimentos, foi proposto um modelo com a troca de valores de parâmetros de Algoritmos Genéticos, em tempo de execução, pela utilização de Sistemas de Inferência Nebulosos, de forma a melhorar o desempenho do sistema, minimizando ambas as medidas citadas anteriormente.This 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.
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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.
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Amaral, 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.
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Orientador: Fernando Antonio Campos GomideDissertação (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
14
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.
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Orientadores: Akebo Yamakami, Marcia Tomie TakahashiDissertação (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
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Rosa, Raul Arthur Fernandes 1989. "Redes neurais evolutivas com aprendizado extremo recursivo." [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259065.
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Orientadores: Fernando Antonio Campos Gomide, Marcos Eduardo Ribeiro do Valle MesquitaDissertação (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
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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.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESOne 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
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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.
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The renewal of derelict inner-city urban districts suffering from high levels of socio-economic deprivation and sustainability problems is one of the key research areas in urban planning and regeneration. Subject to a wide range of social, economical and environmental factors, decision support for an optimal allocation of residential and service lots within such districts is regarded as a complex task. Pre-assessment of various neighbourhood factors before the commencement of actual location allocation of various public services is considered paramount to the sutainable outcome of regeneration projects. Spatial assessment in such derelict built-up areas requires planning of lot assignment for residential buildings in a way to maximize accessibility to public services while minimizing the deprivation of built neighbourhood areas. However, the prediction of socio-economic deprivation impact on the regeneration districts in order to optimize the location-allocation of public service infrastructure is a complex task. This is generally due to the highly conflicting nature of various service structures with various socio-economic and environmental factors. In regards to the problem given above, this thesis presents the development of an evolutionary AI-based decision support systemto assist planners with the assessment and optimization of regeneration districts. The work develops an Adaptive Network Based Fuzzy Inference System (ANFIS) based module to assess neighbourhood districts for various deprivation factors. Additionally an evolutionary genetic algorithms based solution is implemented to optimize various urban regeneration layouts based upon the prior deprivation assessment model. The two-tiered framework initially assesses socio-cultural deprivation levels of employment, health, crime and transport accessibility in neighbourhood areas and produces a deprivation impact matrix overthe regeneration layout lots based upon a trained, network-based fuzzy inference system. Based upon this impact matrix a genetic algorithm is developed to optimize the placement of various public services (shopping malls, primary schools, GPs and post offices) in a way that maximize the accessibility of all services to regenerated residential units as well as contribute to minimize the measure of deprivation of surrounding neighbourhood areas. The outcome of this research is evaluated over two real-world case studies presenting highly coherent results. The work ultimately produces a smart urban regeneration toolkit which provides designer and planner decision support in the form of a simulation toolkit.
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Rashid, Kashif. "Optimisation in electromagnetics using computational intelligence." Thesis, Imperial College London, 2000. http://hdl.handle.net/10044/1/8735.
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Maciel, 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.
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Orientadores: Fernando Antonio Campos Gomide, Rosangela BalliniDissertação (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
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Remias, Michael George. "Computational studies of some fuzzy mathematical problems." Thesis, Curtin University, 2012. http://hdl.handle.net/20.500.11937/1147.
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In modelling and optimizing real world systems and processes, one usually ends up with a linear or nonlinear programming problem, namely maximizing one or more objective functions subject to a set of constraint equations or inequalities. For many cases, the constraints do not need to be satisfied exactly, and the coefficients involved in the model are imprecise in nature and have to be described by fuzzy numbers to reflect the real world nature. The resulting mathematical programming problem is referred to as a fuzzy mathematical programming problem.Over the past decades, a great deal of work has been conducted to study fuzzy mathematical programming problems and a large volume of results have been obtained. However, many issues have not been resolved. This research is thus undertaken to study two types of fuzzy mathematical programming problems. The first type of problems is fuzzy linear programming in which the objective function contains fuzzy numbers. To solve this type of problems, we firstly introduce the concept of fuzzy max order and non-dominated optimal solution to fuzzy mathematical programming problems within the framework of fuzzy mathematics. Then, based on the new concept introduced, various theorems are developed, which involve converting the fuzzy linear programming problem to a four objective linear programming problem of non-fuzzy members. The theoretical results and methods developed are then validated and their applications for solving fuzzy linear problems are demonstrated through examples.The second type of problems which we tackle in this research is fuzzy linear programming in which the constraint equations or inequalities contain fuzzy numbers. For this work, we first introduce a new concept, the α-fuzzy max order. Based on this concept, the general framework of an α-fuzzy max order method is developed for solving fuzzy linear programming problems with fuzzy parameters in the constraints. For the special cases in which the constraints consist of inequalities containing fuzzy numbers with isosceles triangle or trapezoidal membership functions, we prove that the feasible solution space can be determined by the respective 3n or 4n non-fuzzy inequalities. For the general cases in which the constraints contain fuzzy numbers with any other form of membership functions, robust numerical algorithms have been developed for the determination of the feasible solution space and the optimal solution to the fuzzy linear programming problem in which the constraints contain fuzzy parameters. Further, by using the results for both the first and second types of problems, general algorithms have also been developed for the general fuzzy linear programming problems in which both the objective function and the constraint inequalities contain fuzzy numbers with any forms of membership functions. Some examples are then presented to validate the theoretical results and the algorithms developed, and to demonstrate their applications.
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Claessen, 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.
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Kim, Hantae. "FUZZY RULE-BASED RELAXATION SCHEMES IN GROUNDWATER FLOW COMPUTATIONS." Kyoto University, 1998. http://hdl.handle.net/2433/182424.
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Kyoto University (京都大学)0048
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
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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.
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Machine tool components are widely used in many industrial applications. In accordance with their usage, a reliable health monitoring system is necessary to detect defects in these components in order monitor machinery performance and avoid malfunction. Even though several techniques have been reported for fault detection and diagnosis, it is a challenging task to implement a condition monitoring system in real world applications due to their complexity in structure and noisy operating environment. The primary objective of this thesis is to develop novel intelligent algorithms for a reliable fault diagnosis of machine tool components. Another objective is to use Micro Electro Mechanical System (MEMS) sensor and interface it with Raspberry pi hardware for the real time condition monitoring.
Primarily knowledge based approach with morphological operators and Fuzzy Inference System is proposed, the e˙ectiveness of this approach lies in the selection of structuring elements(SEs). When this is evaluated with di˙erent classes of bearing fault signals, it is able to detect the fault frequencies e˙ectively. Secondarily, An analytical approach with multi class support machine is proposed, this method has uniqueness of learning on its own with out any prior knowledge, the e˙ectiveness of this method lies on selected features and used kernel for converging. Results have shown that RBF (Radial Bias Function) kernel, which is commonly known as gauss kernel has good performance in identifying faults with less computation time. An idea of prototyping these methods has triggered in using Micro Electro Mechanical System (MEMS) sensor for data acquisition and real time Condition Monitoring. LIS3DH accelerometer sensor is used for the data acquisition of spindle for capturing high frequency fault signals. The measured data is analyzed and compared with the industrial sensor k-shear accelerometer type 8792A.
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Chuang, Wei Kuo. "Computational model for engineering design and development." Thesis, Brunel University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263496.
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Smit, Marius. "Interactive narrative generation using computational verb theory." Diss., University of Pretoria, 2009. http://hdl.handle.net/2263/27510.
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Interactive narrative extends traditional story-telling techniques by enabling previously passive observers to become active participants in the narrative events that unfold. A variety of approaches have attempted to construct such interactive narrative spaces and reconcile the goals of interactivity and dramatic story-telling. With the advent of the linguistic variable in 1972, a means was established for modelling natural language words and phrases mathematically and computationally. Over the past decade, the computational verb, first introduced in 1997, has been developed as a mathematical means of modelling natural language verbs in terms of dynamic systems, and vice versa. Computational verb theory extends the initial concept of the linguistic variable beyond being able to model adjectives, nouns, and passive states, into the realm of actions as denoted by natural language verbs. This thesis presents the framework and implementation of a system that generates interactive narrative spaces from narrative text. The concept of interactive narrative is introduced and recent developments in the area of interactive narrative are discussed. Secondly, a brief history of the development of the linguistic variable and the computational verb are provided. With the context of the computational verb (interactive) narrative generation (CVTNG) system presented, the underlying theoretical principles of the system are established. The CVTNG system principles are described in terms of fuzzy set, computational verb, and constraint satisfaction theory. The fuzzy set, computational verb, and constraint satisfaction principles are organised according to a CVTNG architecture. The CVTNG architecture is then described in terms of its subsystems, structures, algorithms, and interfaces. Each CVTNG system component is related to the overall design considerations and goals. A prototype of the CVTNG system is implemented and tested against a suite of natural language sentences. The behaviour and performance of the CVTNG system prototype are discussed in relation to the CVTNG system’s design principles. Results are calculated and stored as variable values that are dynamically and generically associated with representational means, specifically computer graphics, to illustrate the generation of interactive narrative spaces. Plans for future work are discussed to show the immense development potential of this application. The thesis concludes that the CVTNG system provides a solid and extendable base for the intuitive generation of interactive narrative spaces from narrative text, computational verb models, and freely associated media. CopyrightDissertation (MSc)--University of Pretoria, 2010.
Computer Science
Unrestricted
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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.
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This doctoral thesis is devoted to investigate the problem of establishing
connections between Domain Theory and the theory of fuzzy metric spaces, in
the sense of Kramosil and Michalek, by means of the notion of a formal ball,
and then constructing topological and computational models for (complete)
fuzzy metric spaces.
The antecedents of this research are mainly the well-known articles of A.
Edalat and R. Heckmann [A computational model for metric spaces, Theoret-
ical Computer Science 193 (1998), 53-73], and R. Heckmann [Approximation
of metric spaces by partial metric spaces, Applied Categorical Structures 7
(1999), 71-83], where the authors obtained nice and direct links between Do-
main Theory and the theory of metric spaces - two crucial tools in the study
of denotational semantics - by using formal balls.
Since every metric induces a fuzzy metric (the so-called standard fuzzy
metric), the problem of extending Edalat and Heckmann's works to the fuzzy
framework arises in a natural way.
In our study we essentially propose two di erent approaches. For the
rst one, valid for those fuzzy metric spaces whose continuous t-norm is
the minimum, we introduce a new notion of fuzzy metric completeness (the
so-called standard completeness) that allows us to construct a (topological)
model that includes the classical theory as a special case. The second one,
valid for those fuzzy metric spaces whose continuous t-norm is greater or
equal than the Lukasiewicz t-norm, allows us to construct, among other
satisfactory results, a fuzzy quasi-metric on the continuous domain of formal
balls whose restriction to the set of maximal elements is isometric to the
given fuzzy metric. Thus we obtain a computational model for complete
fuzzy metric spaces.
We also prove some new xed point theorems in complete fuzzy metric
spaces with versions to the intuitionistic case and the ordered case, respec-
tively.
Finally, we discuss the problem of extending the obtained results to the
asymmetric framework.Ricarte 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
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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.
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Exner, 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.
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Miranda, 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.
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Dissertação apresentada para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores, pela Universidade Nova de Lisboa, Faculdade de Ciências e TecnologiaWith 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.
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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.
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The nature of uncertainty cannot be generically defined as it is domain and context specific. With that being the case, there have been several proposed models, all of which have their own associated benefits and shortcomings. From these models, it was decided that an R-fuzzy approach would provide for the most ideal foundation from which to enhance and expand upon. An R-fuzzy set can be seen as a relatively new model, one which itself is an extension to fuzzy set theory. It makes use of a lower and upper approximation bounding from rough set theory, which allows for the membership function of an R-fuzzy set to be that of a rough set. An R-fuzzy approach provides the means for one to encapsulate uncertain fuzzy membership values, based on a given abstract concept. If using the voting method, any fuzzy membership value contained within the lower approximation can be treated as an absolute truth. The fuzzy membership values which are contained within the upper approximation, may be the result of a singleton, or the vast majority, but absolutely not all. This thesis has brought about the creation of a significance measure, based on a variation of Bayes' theorem. One which enables the quantification of any contained fuzzy membership value within an R-fuzzy set. Such is the pairing of the significance measure and an R-fuzzy set, an intermediary bridge linking to that of a generalised type-2 fuzzy set can be achieved. Simply by inferencing from the returned degrees of significance, one is able to ascertain the true significance of any uncertain fuzzy membership value, relative to other encapsulated uncertain values. As an extension to this enhancement, the thesis has also brought about the novel introduction of grey analysis. By utilising the absolute degree of grey incidence, it provides one with the means to measure and quantify the metric spaces between sequences, generated based on the returned degrees of significance for any given R-fuzzy set. As it will be shown, this framework is ideally suited to domains where perceptions are being modelled, which may also contain several varying clusters of cohorts based on any number of correlations. These clusters can then be compared and contrasted to allow for a more detailed understanding of the abstractions being modelled.
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Conroy, Justin Anderson. "Analysis of adaptive neuro-fuzzy network structures." Thesis, Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/19684.
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Auephanwiriyakul, 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.
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Cruz, Anderson Paiva. "L?gica BDI fuzzy." Universidade Federal do Rio Grande do Norte, 2008. http://repositorio.ufrn.br:8080/jspui/handle/123456789/17995.
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Previous issue date: 2008-09-26Coordena??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)
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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.
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Abdallah, 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.
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The bioreactor landfill is an emerging concept for solid waste management that has gained significant attention in the last decade. This technology employs specific operational practices to enhance the microbial decomposition processes in landfills. However, the unsupervised management and lack of operational guidelines for the bioreactor landfill, specifically leachate manipulation and recirculation processes, usually results in less than optimal system performance. Therefore, these limitations have led to the development of SMART (Sensor-based Monitoring and Remote-control Technology), an expert control system that utilizes real-time monitoring of key system parameters in the management of bioreactor landfills.
SMART replaces conventional open-loop control with a feedback control system that aids the human operator in making decisions and managing complex control issues. The target from this control system is to provide optimum conditions for the biodegradation of the refuse, and also, to enhance the performance of the bioreactor in terms of biogas generation. SMART includes multiple cascading logic controllers and mathematical calculations through which the quantity and quality of the recirculated solution are determined. The expert system computes the required quantities of leachate, buffer, supplemental water, and nutritional amendments in order to provide the bioreactor landfill microbial consortia with their optimum growth requirements.
Soft computational methods, particularly fuzzy logic, were incorporated in the logic controllers of SMART so as to accommodate the uncertainty, complexity, and nonlinearity of the bioreactor landfill processes. Fuzzy logic was used to solve complex operational issues in the control program of SMART including: (1) identify the current operational phase of the bioreactor landfill based on quantifiable parameters of the leachate generated and biogas produced, (2) evaluate the toxicological status of the leachate based on certain parameters that directly contribute to or indirectly indicates bacterial inhibition, and (3) predict biogas generation rates based on the operational phase, leachate recirculation, and sludge addition. The later fuzzy logic model was upgraded to a hybrid model that employed the learning algorithm of artificial neural networks to optimize the model parameters.
SMART was applied to a pilot-scale bioreactor landfill prototype that incorporated the hardware components (sensors, communication devices, and control elements) and the software components (user interface and control program) of the system. During a one-year monitoring period, the feasibility and effectiveness of the SMART system were evaluated in terms of multiple leachate, biogas, and waste parameters. In addition, leachate heating was evaluated as a potential temperature control tool in bioreactor landfills.
The pilot-scale implementation of SMART demonstrated the applicability of the system. SMART led to a significant improvement in the overall performance of the BL in terms of methane production and leachate stabilization. Temperature control via recirculation of heated leachate achieved high degradation rates of organic matter and improved the methanogenic activity.
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Chen, Xiujuan. "Computational Intelligence Based Classifier Fusion Models for Biomedical Classification Applications." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_diss/26.
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The generalization abilities of machine learning algorithms often depend on the algorithms’ initialization, parameter settings, training sets, or feature selections. For instance, SVM classifier performance largely relies on whether the selected kernel functions are suitable for real application data. To enhance the performance of individual classifiers, this dissertation proposes classifier fusion models using computational intelligence knowledge to combine different classifiers. The first fusion model called T1FFSVM combines multiple SVM classifiers through constructing a fuzzy logic system. T1FFSVM can be improved by tuning the fuzzy membership functions of linguistic variables using genetic algorithms. The improved model is called GFFSVM. To better handle uncertainties existing in fuzzy MFs and in classification data, T1FFSVM can also be improved by applying type-2 fuzzy logic to construct a type-2 fuzzy classifier fusion model (T2FFSVM). T1FFSVM, GFFSVM, and T2FFSVM use accuracy as a classifier performance measure. AUC (the area under an ROC curve) is proved to be a better classifier performance metric. As a comparison study, AUC-based classifier fusion models are also proposed in the dissertation. The experiments on biomedical datasets demonstrate promising performance of the proposed classifier fusion models comparing with the individual composing classifiers. The proposed classifier fusion models also demonstrate better performance than many existing classifier fusion methods. The dissertation also studies one interesting phenomena in biology domain using machine learning and classifier fusion methods. That is, how protein structures and sequences are related each other. The experiments show that protein segments with similar structures also share similar sequences, which add new insights into the existing knowledge on the relation between protein sequences and structures: similar sequences share high structure similarity, but similar structures may not share high sequence similarity.
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Costa, 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/.
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A Inteligência Artificial atualmente é um vasto campo de pesquisa. Existem diversas técnicas sendo pesquisadas, sendo que nesta tese foram utilizadas a Teoria Fuzzy, Árvores de Decisão e Redes Neurais. As três técnicas têm sido empregadas com sucesso nas mais diversas aplicações nas áreas de automação e controle, reconhecimento de padrões, reconhecimento de voz, detecção de falhas e classificação, entre outras. A Teoria Fuzzy permite trabalhar com as incertezas e provê um entendimento simbólico para compreensão do conhecimento. As Árvores de Decisão têm capacidade de construir decisões simbólicas para a classificação de problemas e, através do conhecimento obtido, pode-se construir regras simbólicas para uma tomada de decisão. A Teoria Fuzzy também pode ser incorporada às árvores de decisão, aumentando seu poder de representação e aplicabilidade. As Redes Neurais (algoritmo back-propagation) têm apresentado ótimos resultados na aprendizagem de funções e em problemas de classificação. A contribuição desta tese é mostrar a aplicação das três técnicas de Inteligência Artificial (IA) em processos de fabricação de Vidro. Os processos de fabricação do vidro foram analisados e a proposta da tese é a aplicação das técnicas de IA nas fábricas de produção de vidros para embalagens e vidros planos. Na primeira fábrica aplicam-se as técnicas de IA para classificar os defeitos que ocorrem no Vidro para Embalagens, em função das condições operacionais dos fornos de fusão. Na segunda fábrica aplicam-se as técnicas para classificar os defeitos em função das matérias primas utilizadas na produção do vidro. Na terceira fábrica as técnicas são aplicadas na classificação dos padrões de fabricação do vidro plano. Os resultados obtidos com a classificação de defeitos e padrões foram de maneira geral satisfatórios. As três técnicas de IA apresentadas foram utilizadas para a análise das bases de dados nas três fábricas de vidro estudadas nesta tese. As técnicas de IA obtiveram classificações satisfatórias para os defeitos do vidro para embalagens e para classificar os padrões dos vidros planos. Os resultados obtidos a partir das técnicas são comparados e apresentam resultados promissores.The 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.
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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.
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Funsten, Brad Thomas Mr. "ECG Classification with an Adaptive Neuro-Fuzzy Inference System." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1380.
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Heart signals allow for a comprehensive analysis of the heart. Electrocardiography (ECG or EKG) uses electrodes to measure the electrical activity of the heart. Extracting ECG signals is a non-invasive process that opens the door to new possibilities for the application of advanced signal processing and data analysis techniques in the diagnosis of heart diseases. With the help of today’s large database of ECG signals, a computationally intelligent system can learn and take the place of a cardiologist. Detection of various abnormalities in the patient’s heart to identify various heart diseases can be made through an Adaptive Neuro-Fuzzy Inference System (ANFIS) preprocessed by subtractive clustering. Six types of heartbeats are classified: normal sinus rhythm, premature ventricular contraction (PVC), atrial premature contraction (APC), left bundle branch block (LBBB), right bundle branch block (RBBB), and paced beats. The goal is to detect important characteristics of an ECG signal to determine if the patient’s heartbeat is normal or irregular. The results from three trials indicate an average accuracy of 98.10%, average sensitivity of 94.99%, and average specificity of 98.87%. These results are comparable to two artificial neural network (ANN) algorithms: gradient descent and Levenberg Marquardt, as well as the ANFIS preprocessed by grid partitioning.
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Abel, Andrew. "Towards an intelligent fuzzy based multimodal two stage speech enhancement system." Thesis, University of Stirling, 2013. http://hdl.handle.net/1893/15989.
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This thesis presents a novel two stage multimodal speech enhancement system, making use of both visual and audio information to filter speech, and explores the extension of this system with the use of fuzzy logic to demonstrate proof of concept for an envisaged autonomous, adaptive, and context aware multimodal system. The design of the proposed cognitively inspired framework is scalable, meaning that it is possible for the techniques used in individual parts of the system to be upgraded and there is scope for the initial framework presented here to be expanded. In the proposed system, the concept of single modality two stage filtering is extended to include the visual modality. Noisy speech information received by a microphone array is first pre-processed by visually derived Wiener filtering employing the novel use of the Gaussian Mixture Regression (GMR) technique, making use of associated visual speech information, extracted using a state of the art Semi Adaptive Appearance Models (SAAM) based lip tracking approach. This pre-processed speech is then enhanced further by audio only beamforming using a state of the art Transfer Function Generalised Sidelobe Canceller (TFGSC) approach. This results in a system which is designed to function in challenging noisy speech environments (using speech sentences with different speakers from the GRID corpus and a range of noise recordings), and both objective and subjective test results (employing the widely used Perceptual Evaluation of Speech Quality (PESQ) measure, a composite objective measure, and subjective listening tests), showing that this initial system is capable of delivering very encouraging results with regard to filtering speech mixtures in difficult reverberant speech environments. Some limitations of this initial framework are identified, and the extension of this multimodal system is explored, with the development of a fuzzy logic based framework and a proof of concept demonstration implemented. Results show that this proposed autonomous,adaptive, and context aware multimodal framework is capable of delivering very positive results in difficult noisy speech environments, with cognitively inspired use of audio and visual information, depending on environmental conditions. Finally some concluding remarks are made along with proposals for future work.
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Zarei, 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.
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Pereira, 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/.
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O Sistema Proteu Fuzzy é um sistema de tomada de decisão para compra e venda de ativos financeiros que visa auxiliar a figura do analista técnico (de modo imparcial e racional), informando quando existe uma boa oportunidade para se comprar ou vender um determinado ativo (e.g. ações). Utilizaram-se, como base para as suas decisões, técnicas de inteligência artificial (Lógica Fuzzy) e indicadores técnicos (Médias Móveis, MACD e RSI). As simulações mostram que o sistema conseguiu gerar resultados de forma consistente e com menor volatilidade que o mercado para alguns ativos.The 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.
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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.
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2011 - 2012The 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]
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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.
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Sá, 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/.
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Este trabalho de dissertação apresenta um método baseado em técnicas de inteligência computacional, como aprendizado de conjunto de regras, redes neurais artificiais e lógica fuzzy, para propor o desenvolvimento de ferramentas capazes de gerar e classificar casos de testes de caixa preta com as finalidades de auxiliar na atividade de preparação de testes, na detecção de defeitos em características ou funcionalidades e na diminuição do tempo de detecção de correção do software visando, com isto, atingir uma cobertura de testes qualitativamente superior ao processo criação manual. A obtenção de novos casos de testes e a classificação dos casos de testes gerados utilizam técnicas de aprendizado de um conjunto de regras, utilizando algoritmos de cobertura seqüencial, e de uma máquina de inferência fuzzy. A definição dos métodos, tanto para gerar como para classificar os casos de testes, foram fundamentados em experimentos visando comparar as similaridades entre os métodos fuzzy, redes neurais artificiais e aprendizado de conjunto de regras. Por fim, procurou-se desenvolver uma ferramenta à titulo de prova de conceitos objetivando aplicar os métodos que obtiveram melhores resultados nas experimentações. Os critérios adotados para definir os métodos foram às métricas de complexidade ciclomática e total de linhas de código (LOC).This 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).
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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.
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Due to complexity of biomedical problems, adaptive and intelligent knowledge discovery and data mining systems are highly needed to help humans to understand the inherent mechanism of diseases. For biomedical classification problems, typically it is impossible to build a perfect classifier with 100% prediction accuracy. Hence a more realistic target is to build an effective Decision Support System (DSS). In this dissertation, a novel adaptive Fuzzy Association Rules (FARs) mining algorithm, named FARM-DS, is proposed to build such a DSS for binary classification problems in the biomedical domain. Empirical studies show that FARM-DS is competitive to state-of-the-art classifiers in terms of prediction accuracy. More importantly, FARs can provide strong decision support on disease diagnoses due to their easy interpretability. This dissertation also proposes a fuzzy-granular method to select informative and discriminative genes from huge microarray gene expression data. With fuzzy granulation, information loss in the process of gene selection is decreased. As a result, more informative genes for cancer classification are selected and more accurate classifiers can be modeled. Empirical studies show that the proposed method is more accurate than traditional algorithms for cancer classification. And hence we expect that genes being selected can be more helpful for further biological studies.
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Lin, Shang-chih, and 林尚志. "Apply Fuzzy Computation to Time-Frequency Analysis." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/91418979635887337627.
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碩士大同大學
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.
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Wan, Yu-en, and 萬又恩. "Computation on solutions of fuzzy linear systems." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/15633424623149905332.
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碩士國立臺南大學
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.
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(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.
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"The research presented in this thesis examines the construction of a fuzzy logic controller for complex nonlinear system by control system decomposition into hierarchial fuzzy logic subsystems ... evolutionary algorithm (EA) based methods are proposed to determine the control system for the hieracrchical fuzzy system (HFS)"--Abstract.
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(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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In this thesis we investigate how artificial intelligent techniques, namely fuzzy logic and genetic/evolutionary algorithms can be used for digital image processing applications. We demonstrate our techniques with respect to two main research areas: removal of heavy impulse noise from corrupted gray scale images and edge detection in digital images. Very often fuzzy logic systems need to deal with large number of rules. This results in two major design issues: (i) How to formulate the fuzzy knowledge base using human expertise and experience? (ii) How to reduce the high computational power and the high processing times required? In this thesis we use evolutionary algorithms (including coevolutionary algorithms) to learn fuzzy knowledge bases to handle the design issue (i) described above, while using multi-layered and hierarchical fuzzy logic systems to reduce the number of rules and hence the computational overhead involved, thereby addressing issue (ii) stated above. In this research, when fuzzy rules are learnt using evolutionary algorithms, each individual in the evolutionary algorithm is appropriately encoded to uniquely represent the fuzzy knowledge base. The fitness of each individual in the evolutionary algorithm is calculated with respect to a predefined reference. In the case of an algorithm learning to enhance a digital image this reference is often associated with the uncorrupted perfect image. Designing multi-layered and hierarchical fuzzy structures involves breaking down the total number of rules, to be fed into multiple fuzzy layers in the system. This process needs careful consideration in forming the appropriate fuzzy layers as well as deciding the parameters to be input to different layers, so that the desired result is obtained with highest precision using the least computation time. Coevolutionary algorithms are powerful tools that can be used in situations where several factors contributing towards the system performance need to be learnt simultaneously. Here multiple populations consisting of candidate solutions are evolved in parallel and the fitness of individuals in each of the population are evaluated by forming a vector of candidate solutions selected from each population. The artificial intelligence techniques briefly described above will be used in this thesis with application to enhancement and edge detection in digital images.