Mitigation of displacement and acceleration responses of a three story benchmark structure excited by seismic motions is pursued in this study. Multiple 20-kN magnetorheological (MR) dampers are installed in the three-story benchmark structure and managed by a global fuzzy logic controller to provide smart damping forces to the benchmark structure. Two configurations of MR damper locations are considered to display multiple-input, single-output and multiple-input, multiple-output control capabilities. Characterization tests of each MR damper are performed in a laboratory to enable the formulation of fuzzy inference models. Prediction of MR damper forces by the fuzzy models shows sufficient agreement with experimental results. A controlled-elitist multi-objective genetic algorithm is utilized to optimize a set of fuzzy logic controllers with concurrent consideration to four structural response metrics. The genetic algorithm is able to identify optimal passive cases for MR damper operation, and then further improve their performance by intelligently modulating the command voltage for concurrent reductions of displacement and acceleration responses. An optimal controller is identified and validated through numerical simulation and fullscale experimentation. Numerical and experimental results show that performance of the controller algorithm is superior to optimal passive cases in 43% of investigated studies. Furthermore, the state-space model of the benchmark structure that is used in numerical simulations has been improved by a modified version of the same genetic algorithm used in development of fuzzy logic controllers. Experimental validation shows that the state-space model optimized by the genetic algorithm provides accurate prediction of response of the benchmark structure to base excitation.

As the complexity of a spatial phenomenon increases, traditional modeling becomes impractical. Alternatively, data-driven modeling, which is based on the analysis of data characterizing the phenomena, can be used. In this thesis, the generation of understandable and reliable spatial models using observational data is addressed. An interpretability oriented data-driven fuzzy modeling approach is proposed. The methodology is based on construction of fuzzy models from data, tuning and fuzzy model simplification. Mamdani type fuzzy models with triangular membership functions are considered. Fuzzy models are constructed using fuzzy clustering algorithms and simulated annealing metaheuristic is adapted for the tuning step. To obtain compact and interpretable fuzzy models a simplification methodology is proposed. Simplification methodology reduced the number of fuzzy sets for each variable and simplified the rule base. Prototype software is developed and mean annual precipitation data of Turkey is examined as case study to assess the results of the approach in terms of both precision and interpretability. In the first step of the approach, in which fuzzy models are constructed from data, &quot / Fuzzy Clustering and Data Analysis Toolbox&quot / , which is developed for use with MATLAB, is used. For the other steps, the optimization of obtained fuzzy models from data using adapted simulated annealing algorithm step and the generation of compact and interpretable fuzzy models by simplification algorithm step, developed prototype software is used. If the accuracy is the primary objective then the proposed approach can produce more accurate solutions for training data than geographically weighted regression method. The minimum training error value produced by the proposed approach is 74.82 mm while the error obtained by geographically weighted regression method is 106.78 mm. The minimum error value on test data is 202.93 mm. An understandable fuzzy model for annual precipitation is generated only with 12 membership functions and 8 fuzzy rules. Furthermore, more interpretable fuzzy models are obtained when Gath-Geva fuzzy clustering algorithms are used during fuzzy model construction.

Une grande d’attentions ont été accordée au cours des années récentes à l’étude de l'économie souterraine dans de nombreux pays développés et en voie de développement. Les conséquences et les implications politiques associées à cette partie ambiguë de l’économie ont suscité des inquiétudes parmi les économistes et les gouvernements qui ont été amenés à proposer diverses mesures et méthodes d’estimation. Il n’est cependant pas facile d’évaluer avec exactitude et précision la taille (l’ampleur) et la tendance de l’économie souterraine à cause de sa nature cachée (dissimulée, discrète). Néanmoins, certaines techniques ont été utilisées par les économistes pour estimer directement ou indirectement la taille de l’économie souterraine. Dans ce manuscrit de thèse, nous nous intéressons à l’économie souterraine comme un phénomène universel ayant une manifestation unique et incontournable sous forme d’activités à la fois légales et illégales. Le but principal de cette thèse est de proposer des « méthodes floues » (méthode de la logique floue) pour en mesurer la taille et la quantité. Dans un premier temps, nous avons construit un cadre conceptuel nous permettant d’étudier les spécificités de l’économie souterraine. Ensuite, de nombreuses (plusieurs, différentes…) méthodes communes d’estimation (méthodes en vigueur pour l’estimation) de l’économie souterraine ont été examinées. Les conditions de base de l’application du concept flou (de la logique floue) ainsi que les conditions initiales de l’économie souterraine ont été explorées (interrogées) pour voir si elles correspondent les unes aux autres (si elles font la paire, si elles sont cohérentes). Dès lors que la logique floue permet une modélisation rapide même avec des données imprécises et incomplètes et des fonctions non-linéaires d’une complexité arbitraire et d’atteindre la simplicité et la flexibilité, nous avons été encouragés à appliquer cette méthode. Trois méthodes floues ont été proposées pour évaluer l’économie souterraine sur la base des enquêtes initiales réalisées pendant la période 1985-2010, à savoir : la modélisation floue appliquant la moyenne et la variance, la modélisation floue appliquant le regroupement flou, la modélisation floue utilisant de multiples indicateurs et causes avec des données floues. En dernier lieu, la taille de l’économie souterraine a été mesurée pour la France, l’Allemagne, l’Italie, les Etats-Unis et le Canada. Les résultats issus de ces différentes méthodes floues ont ensuite été comparés avec ceux obtenus par d’autres méthodes conventionnelles. Il peut être affirmé que les méthodes proposées dans ce travail de recherche sont qualitativement comparables avec les méthodes couramment utilisées pour évaluer l’économie souterraine. / Much attention has been paid in recent years to the study of the underground economy in many developed and developing countries. The consequences and the policy implications linked with this ambiguous part of the economy have raised concerns among economists and governments that led to proposing various measures and estimation methods. It is not however easy to estimate accurately and precisely the size and trend of the underground economy due to its hidden nature. But, some techniques have been used by economists to directly and indirectly estimate the size of the underground economy.In this thesis, we have focused on the underground economy as a universal phenomenon having its unique and inescapable manifestation in the form of both legal and illegal activities. The main purpose of this thesis is to propose the fuzzy methods to measure the size and the amount of the underground economy.We first constructed a conceptual framework that allowed us to study the specifications of the underground economy. The common numerous estimation methods of the underground economy and their weaknesses and strengths were reviewed then. The basic conditions of applying the fuzzy concept and the initial conditions of the underground economy were investigated to see if they match. Since fuzzy modeling allows for rapid modeling even with imprecise and incomplete data and lets us to model non-linear functions of arbitrary complexity and achieve simplicity and flexibility, we were encouraged to apply fuzzy method.Two fuzzy methods were proposed to estimate the underground economy based on initial investigations during the period 1985-2010, including: the fuzzy modeling applying mean and standard deviation, the fuzzy modeling applying fuzzy clustering and Multiple Indicators and Multiple Causes (Structural Equation Modeling) with Fuzzy Data.Finally, the size of the underground economy was measured for France, Germany, Italy, USA and Canada and the results of these fuzzy methods were compared with other conventional methods. It can be claimed that the proposed methods in this work are qualitatively comparable to the common methods used to estimate the underground economy.

Orientador: Prof. Dr. Daniel Miranda Machado / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Mestrado Profissional em Matemática em Rede Nacional, 2017. / Este trabalho têm como objetivo demonstrar a utilização da modelagem em lógica fuzzy na tomada de decisões, em particular na escolha da taxa SELIC de modo antecipado, utilizando alguns dos parâmetros que servem para a decisão pelo COPOM (Comitê de política monetária), ao final poderemos verificar que o resultado real é compatível com o resultado fuzzy, podendo portanto este tipo de modelagem ser utilizada em outras situações. Também veremos a utilização da lógica fuzzy para controle de proximidades, quando temos uma incerteza de quanto um objeto estará perto de um obstáculo, como resultado veremos através de algumas execuções do programa que será mais adequado utilizar a lógica fuzzy do que a lógica clássica. Para essas aplicações utilizaremos as linguagens de programação R e Kturtle, sendo um diferencial pois são gratuitas e possibilitam a criação de outras aplicações. Teremos antes a demostração da teoria da lógica fuzzy necessária para as aplicações propostas. / The purpose of this dissertation is to demonstrate the use of fuzzy logic modeling in decision making, particularly in the selection of the SELIC rate in advance, using some of the parameters that serve for the decision by the COPOM (Monetary Policy Committee). We verify that the actual result is compatible with the fuzzy result, so that this type of modeling can be used in other situations. We will also see the use of fuzzy logic for neighborhood control when we have an uncertainty about how much an object will be close to an obstacle, as a result we will see through some program executions that it will be more appropriate to use fuzzy logic than classical logic. For these applications we will use the programming languages R and Kturtle, being an advantage because they are free and allow the creation of other applications. We will first develop of the fuzzy logic theory required for the proposed applications.

Submitted by Vinicius Machado Martinez null (vinicius@ipmet.unesp.br) on 2016-03-11T20:20:53Z No. of bitstreams: 1 defesa.pdf: 4478778 bytes, checksum: 15d0e211ad6c8ad44eabe8079cf63fa2 (MD5) / Approved for entry into archive by Juliano Benedito Ferreira (julianoferreira@reitoria.unesp.br) on 2016-03-15T17:11:38Z (GMT) No. of bitstreams: 1 martinez_vm_me_soro.pdf: 4478778 bytes, checksum: 15d0e211ad6c8ad44eabe8079cf63fa2 (MD5) / Made available in DSpace on 2016-03-15T17:11:38Z (GMT). No. of bitstreams: 1 martinez_vm_me_soro.pdf: 4478778 bytes, checksum: 15d0e211ad6c8ad44eabe8079cf63fa2 (MD5) Previous issue date: 2016-01-25 / O campo de refletividade de ecos observados por radares meteorológicos está sujeito a interferências de fenômenos físicos da atmosfera que podem resultar em interpretações não realísticas do fenômeno observado. Buscando ajustar o campo de refletividade de ecos detectados simultaneamente por dois radares meteorológicos ao longo da distância, este estudo desenvolveu um sistema baseado em regras fuzzy (SBRF), do tipo Takagi-Sugeno de primeira ordem, que combina as variáveis distância (km) e refletividade (dBZ) para expressar a refletividade ajustada de alvos mais distantes de um radar em relação a outro radar mais próximo, de modo que os efeitos das interferências nas medidas dos radares possam ser minimizados. Os dados utilizados são oriundos de dois radares meteorológicos do IPMet/UNESP, localizados nos municípios de Bauru (22,3583° S; 49,0278° W) e Presidente Prudente (22.175°1 S; 51.3743° W), no Brasil, no período de um ano de dados (2010) do produto CAPPI, faixa de 3.5km de altitude. A saída do sistema é nomeada refletividade fuzzy (RF) e é obtida através de um conjunto de nove curvas de regressão linear, cujos coeficientes angulares e lineares foram estimados pelo método dos mínimos quadrados. Dois parâmetros foram utilizados para análise das curvas obtidas: o coeficiente de correlação de Pearson e o coeficiente de determinação. O sistema foi aplicado a 20.514 dados referentes a 18 pixels distribuídos sobre uma faixa de comum cobertura dos radares. O modelo foi avaliado através das estatísticas de erro médio (BIAS), erro quadrático médio (MSE) e pelo teste de Kolmogorov-Smirnov. Os resultados obtidos demonstram a capacidade do sistema em aproximar o campo de refletividade de dois radares que operam sobre uma área de comum cobertura, devendo constituir-se como uma ferramenta alternativa de interpretação no monitoramento de chuvas, nos processos de modelagem ambiental e em sugestões futuras de estimativa de chuva por radar. / The reflectivity field of echoes observed by weather radars is subjected to the interference of physical phenomena of the atmosphere that can result in unrealistic interpretations on its characteristics. Aiming to adjust the reflectivity field of echoes simultaneously detected by two weather radars over a distance, this study developed a Takagi-Sugeno fuzzy rule-based system, whose input variables are the distance(km) and the reflectivity(dBZ) of the echoes, in order to obtain the adjusted reflectivity of echoes more distant from a radar in relation to another closer radar that is considered to be less susceptible to interference and, therefore, more realistic. The data used are from two weather radars of IPMet/UNESP, located in the municipalities of Bauru (22.3583° S; 49.0278° W) and Presidente Prudente (22.1751° S; 51.3743° W), Brazil, which collected data of the CAPPI product in the period of one year (2010), with a sampling altitude range of 3.5 km. The system output is named “Fuzzy Reflectivity” (FR), obtained through the fuzzy approach of a set of nine linear regression curves, whose angular and linear coefficients were estimated by the method of least squares. Two parameters were used for the analysis of the curves obtained: the Pearson correlation coefficient and the coefficient of determination. The system was applied to 20,514 data related to 18 pixels spread over a range of common coverage of the radars. We evaluated the system performance by means of statistical parameters: average error (bias), mean square error (MSE) and Kolmogorov-Smirnov test. The results obtained demonstrate the system ability to refine the modeling of the issue in question in relation to the traditional statistical approaches, properly adjusting the reflectivity field of the echoes observed by two radars that operate over an area of common coverage and serving as an alternative interpretation tool in the monitoring of rains, in processes of environmental modeling and in future suggestions of rain estimate by radar.

Made available in DSpace on 2016-06-02T19:05:32Z (GMT). No. of bitstreams: 1 2113.pdf: 882226 bytes, checksum: 022c380c1d469988d9e4617a030f17c3 (MD5) Previous issue date: 2008-08-06 / The combination of fuzzy systems with clustering algorithms has great acceptance in the scientific community mainly due to its adherence to the advantage balance principle of computational intelligence, in which different methodologies collaborate with each other potentializing the usefulness and applicability of the resulting systems. Fuzzy Modeling using clustering algorithms presents the transparency and comprehensibility typical of the linguistic fuzzy systems at the same time that benefits from the possibilities of dimensionality reduction by means of clustering. In this work is presented the Fuzzy-CCM method (Fuzzy Conditional Clustering based Modeling) which consists of a new approach for Fuzzy Modeling based on the Fuzzy Conditional Clustering algorithm aiming at providing new means to address the topic of interpretability of fuzzy rules bases. With the Fuzzy-CCM method the balance between interpretability and accuracy of fuzzy rules is dealt with through the definition of contexts defined by a small number of input variables and the generation of clusters induced by these contexts. The rules are generated in a different format, with linguistic variables and clusters in the antecedent. Some experiments have been carried out using different knowledge domains in order to validate the proposed approach by comparing the results with the ones obtained by the Wang&Mendel and conventional Fuzzy C-Means methods. The theoretical foundations, the advantages of the method, the experiments and results are presented and discussed. / A combinação de sistemas fuzzy com algoritmos de agrupamento tem grande aceitação na comunidade científica devido; principalmente, a sua aderência ao princípio de balanceamento de vantagens da inteligência computacional, no qual metodologias diferentes colaboram entre si, potencializando a utilidade e aplicabilidade dos sistemas resultantes. A modelagem fuzzy usando algoritmos de agrupamento apresenta a transparência e facilidade de compreensão típica dos sistemas fuzzy lingüísticos ao mesmo tempo em que se beneficia das possibilidades de redução da dimensionalidade por intermédio do agrupamento. Neste trabalho é apresentado o método Fuzzy-CCM (Fuzzy Conditional Clustering based Modeling), que consiste de uma nova abordagem de Modelagem Fuzzy baseada no algoritmo de Agrupamento Fuzzy Condicional, cujo objetivo é prover novos meios de tratar a questão da interpretabilidade de bases de regras fuzzy. Com o método Fuzzy-CCM, o balanço entre interpretabilidade e acuidade de regras fuzzy é tratado por meio da definição de contextos formados com um pequeno número de variáveis de entrada e a geração de grupos condicionados por estes contextos. As regras são geradas em um formato diferente, que contêm variáveis lingüísticas e grupos no seu antecedente. Alguns experimentos foram executados usando diferentes domínios de conhecimento a fim de validar a abordagem proposta, comparando os resultados obtidos usando a nova abordagem com os resultados obtidos usando os métodos Wang&Mendel e Fuzzy C-Means. A fundamentação teórica, as vantagens do método, os experimentos e os resultados obtidos são apresentados e discutidos.

Made available in DSpace on 2016-08-17T14:53:19Z (GMT). No. of bitstreams: 1 dissertacao Joabe Amaral.pdf: 1492594 bytes, checksum: 667940ee64ccf9cbf29cbf0ed1db27a0 (MD5) Previous issue date: 2012-02-27 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / In this dissertation, a robust fuzzy PID Takagi-Sugeno control methodology based on gain and phase margins specifications for dynamic systems with time delay in continuous time domain is proposed. A fuzzy model based on the Takagi-Sugeno structure is used to represent the dynamic system to be controlled. Thus, from the input and output data of the dynamic system, the Gustafson-Kessel fuzzy clustering algorithm is used to estimate the parameters of the antecedent proposition (input space) and the rules number of the fuzzy model, while the least mean squares algorithm is used to estimate the parameters of the sub-linear models of the consequent proposition (output space) of the fuzzy model. A mathematical formulation based on PDC (parallel and distributed compensation) strategy is defined from the gain and phase margins specifications for the calculation of PID controllers sub-parameters, in the robust fuzzy PID controller rule base, the linear sub-models parameters of the dynamic system model fuzzy rule base to be controlled. An analysis of necessary and sufficient conditions for robust fuzzy PID controller design, with the proposal of one axiom and two theorems are presented. Computational results to validation of the proposal compared to others control methods widely cited in the literature, with the application in the angular position control of a robotic manipulator, are also presented. / Nesta dissertação é proposta uma metodologia de controle PID nebuloso robusto baseado nas especificações das margens de ganho e fase, para sistemas dinâmicos com atraso, no domínio do tempo contínuo. Um modelo nebuloso com estrutura Takagi-Sugeno é utilizado para representar o sistema dinâmico a ser controlado. Assim, a partir dos dados de entrada e saída do sistema dinâmico, o algoritmo de agrupamento nebuloso Gustafson-Kessel é utilizado para estimar os parâmetros da proposição no antecedente (espaço de entrada) e o número de regras do modelo nebuloso, enquanto que o algoritmo de mínimos quadrados é utilizado para estimar os parâmetros dos sub-modelos lineares da proposição no consequente (espaço de saída) do modelo nebuloso. Uma formulação matemática fundamentada na estratégia de Compensação Paralela e Distribuída (PDC) é definida, a partir das especificações das margens de ganho e fase, para o cálculo dos parâmetros dos sub-controladores PID, na base de regras do controlador PID nebuloso robusto, em função dos parâmetros dos sub-modelos lineares na base de regras do modelo nebuloso do sistema dinâmico a ser controlado. Uma análise das condições necessárias e suficientes de projeto do controlador PID nebuloso robusto, com a proposta de um axioma e dois teoremas, são apresentados. Resultados computacionais para a validação da metodologia proposta comparada a dois métodos de controle nebuloso propostos por Teixeira e Zak (1999) e Wang, Tanaka e Griffin (1996), amplamente utilizados na literatura, com aplicação ao problema de controle de posição angular de um manipulador robótico, também são apresentados.

Submitted by Marlene Santos (marlene.bc.ufg@gmail.com) on 2014-09-24T21:03:59Z No. of bitstreams: 2 finalfinal.pdf: 9639130 bytes, checksum: f602829a491b238a34d40c598dc5893a (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2014-09-25T10:32:28Z (GMT) No. of bitstreams: 2 finalfinal.pdf: 9639130 bytes, checksum: f602829a491b238a34d40c598dc5893a (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2014-09-25T10:32:28Z (GMT). No. of bitstreams: 2 finalfinal.pdf: 9639130 bytes, checksum: f602829a491b238a34d40c598dc5893a (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2014-06-26 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Inthispaperweproposeafuzzymodel,calledFuzzyLMScomAutocorrela¸c˜aoMultifractal, whose weights are updated according to information from multifractal traffic modeling. These weights are calculated by incorporating an analytical expression for the autocorrelation function of a multifractal model in the training algorithm of the fuzzy model that is based on the Wiener-Hopf filter. We evaluate the prediction performance of the proposed network traffic prediction algorithm with respect to other predictors. Further, we propose a bandwidth allocation scheme for network traffic based on the fuzzy prediction algorithm. Comparisons with other bandwidth allocation schemes in terms of byte loss rate, link utilization, buffer occupancy and average queue size verifies the efficiency of the proposed scheme. Also, We propose an other adaptive fuzzy algorithm, called Fuzzy-LMS-OBF com alfa adaptivo , for traffic flow control described by theβMWM model. The proposed algorithm uses Orthonormal Basis Functions (OBF) and its training based on the LMS algorithm. We also present an expression for the optimal traffic source rate derived from Fuzzy LMS. Then, we evaluate the performance of the Fuzzy-LMS-OBF com alfa adaptivo algorithm with respect to other methods. Through simulations, we show that the proposed control scheme is benefited from the superior performance of the proposed fuzzy algorithm. Comparisons with other methods in terms of mean and variance of the queue size in the buffer, Utilization rate of the link, Loss rate and Throughput are presented. / Neste trabalho propomos um modelo fuzzy, nomeado Fuzzy LMS com Autocorrela¸c˜ao Multifractal, cujos pesos s˜ao calculados atrav´es de informa¸c˜oes provindas da an´alise multifractal de s´eries temporais. Esses pesos s˜ao encontrados incorporando uma express˜ao anal´ıtica para a fun¸c˜ao de autocorrela¸c˜ao de um modelo multifractal no algoritmo de treinamento do modelo fuzzy que tem como base o filtro de Wiener-Hopf. Avaliamos ent˜ao o desempenho de predi¸c˜ao de tr´afego de redes do modelo fuzzy proposto adaptativo com rela¸c˜ao a outros preditores. Em seguida, propomos um esquema de aloca¸c˜ao de banda para tr´afego de redes baseado no algoritmo Fuzzy LMS com Autocorrela¸c˜ao Multifractal. Compara¸c˜oes com outros esquemas de aloca¸c˜ao de banda em termos de taxa de perda de bytes, utiliza¸c˜ao do enlace, ocupa¸c˜ao do buffer e tamanho m´edio da fila comprovam a eficiˆencia do algoritmo no esquema utilizado. Al´em disso, propomos um outro algoritmo fuzzy adaptativo para controle de fluxos de tr´afego que podem ser descritos pelo modelo multifractalβMWM, que chamamos de Fuzzy-LMS-OBF com alfa adaptivo, o qual utiliza Fun¸c˜oes de Bases Ortonormal (FBO) e tem como base de treinamento, o algoritmo LMS. Propomos tamb´em uma equa¸c˜ao para c´alculo da taxa ´otima de controle derivada do modelo Fuzzy LMS. Em seguida, avaliamos o desempenho do algoritmo de controle adaptativo proposto com rela¸c˜ao a outros m´etodos. Atrav´es de simula¸c˜oes, mostramos que os esquemas de controle e aloca¸c˜ao de taxa se favorecem do desempenho dos algoritmos fuzzy adaptativos propostos. Compara¸c˜oes com outros m´etodos em termos de tamanho m´edio e variˆancia da fila no buffer, Taxa de Utiliza¸c˜ao do enlace e Vaz˜ao s˜ao apresentadas.

CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico / Esse trabalho propõe a modelagem e o controle neuro-fuzzy aplicados na estabilidade estática de um veículo de duas rodas do tipo pêndulo invertido, utilizando como método de otimização a evolução diferencial. Durante a fase de modelagem, determinam-se as incertezas relacionadas aos parâmetros e também à resposta do modelo neuro-fuzzy. Verifica-se que este é capaz de se ajustar satisfatoriamente aos dados extraídos experimentalmente do veículo. Na determinação do controlador neuro-fuzzy, testam-se três estratégias de ajuste de parâmetros, sendo duas delas propostas neste texto, e os resultados são comparados entre si e aos obtidos através de controladores clássicos, e verifica-se experimentalmente e por meio de testes estatísticos que as abordagens propostas apresentam grande capacidade de adaptação às restrições impostas à planta, garantindo a estabilidade estática e a eficiência energética do sistema. / This work proposes the neuro-fuzzy modeling and control applied to the static stability of a two-wheeled inverted pendulum vehicle, using differential evolution as optimization technique. During the modeling phase, the uncertainties related to the parameters and also to the neuro-fuzzy model response are determined. It is possible to verify that the neuro-fuzzy system is capable of satisfactorily adjusts to the data experimentally extracted from the vehicle. In the determination of the neuro-fuzzy controller, three strategies of parameter adjustment are tested, two of them being proposed in this text, and the results are compared between them and those obtained through classical controllers, and it is verified experimentally and through tests that the proposed approaches present a great capacity to adapt to the constraints imposed on the plant, guaranteeing the static stability and the energy efficiency of the system. / Dissertação (Mestrado)

Made available in DSpace on 2016-08-17T14:53:13Z (GMT). No. of bitstreams: 1 Newton_ Teixeira _do_ Nascimento Junior.pdf: 3217306 bytes, checksum: c4c1a1e0f923de4227056ce0050d06f8 (MD5) Previous issue date: 2010-10-05 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / This paper presents a computational method to monitor the operating state of steady electric power transformers in a real time and perspectives of operation over time trends and their operation. The methodology consists of two main steps. The first step is building composed of a block of fuzzy inference s block that can monitor real-time values of transformer s electrical parameters of the transformer (current, voltage between phases, power, oil temperature, winding temperature), to analyze and map these inputs into a single output that reflects what has been defined as operational status of the transformer. We defined five regions of transformer s operation by which a processor can work: emergency, urgent, warning, stable and great. The second step analyze the trend of increase / decrease of operating state obtained from the fuzzy block in a given period, as one day, a month, one year and / or several years. This trend can be characterized in various ways, such as increasing, decreasing very constant. Statistical methods are employed for this analysis. The methodology was evaluated on a database of a large company of generation and transmission of electric energy from the Brazilian electricity sector. The results were satisfactory in that the development of operation of such equipment was qualitatively mapped and their tendencies well characterized. Conceptually, the analytical model presented can be extended to multiple processing units, electric and other functions for up to a full network of interest, allowing subsidize the operation of this and can indicate the need for any future system reinforcements. / Este trabalho apresenta uma metodologia computacional para monitorar o estado operacional em regime permanente de transformadores de potência elétrica ao longo do tempo e suas respectivas tendências de operação. A metodologia é composta de duas etapas principais. Na primeira etapa é construído um bloco de inferência fuzzy capaz de monitorar em tempo real os valores de variáveis elétricas do transformador (corrente, tensão entre fases, potência, temperatura do óleo e temperatura dos enrolamentos), analisar e mapear estas entradas em uma única saída que reflete o que foi definido como estado operacional do transformador. Foram definidas cinco regiões de operação pelas quais um transformador pode trabalhar: emergência, urgência, advertência, estável e ótimo. A segunda etapa trata de analisar a tendência crescimento/decrescimento desse estado operativo obtido do bloco fuzzy em um determinado período, tal como um dia, um mês, um ano e/ou vários anos. Essa tendência pode ser caracterizada de várias formas, tais como crescente, muito decrescente, constante. Métodos estatísticos são empregados para realizar essa análise. A metodologia foi avaliada com uma base de dados de uma grande empresa geradora e transmissora de energia do setor elétrico brasileiro. Os resultados alcançados foram satisfatórios, na medida em que a evolução de operação desses equipamentos foi qualitativamente mapeada e suas tendências bem caracterizadas. Conceitualmente, o modelo de análise apresentado pode ser estendido para várias unidades transformadoras, para outras funções elétricas e até para uma rede de interesse completa, permitindo subsidiar a operação desta e dar indicativos de necessidade de reforços futuros no sistema.