dc.contributorAraújo, Fábio Meneghetti Ugulino de
dc.contributor
dc.contributor
dc.contributorhttp://lattes.cnpq.br/5473196176458886
dc.contributorMaitelli, André Laurindo
dc.contributor
dc.contributorhttp://lattes.cnpq.br/0477027244297797
dc.contributorNascimento Júnior, Cairo Lúcio
dc.contributor
dc.contributorhttp://lattes.cnpq.br/0425874008159542
dc.contributorSilva, Gilbert Azevedo da
dc.contributor
dc.contributorhttp://lattes.cnpq.br/8000184133806404
dc.contributorGabriel Filho, Oscar
dc.contributor
dc.contributorhttp://lattes.cnpq.br/4171033998524192
dc.creatorRodrigues, Marconi Câmara
dc.date.accessioned2010-09-09
dc.date.accessioned2014-12-17T14:54:55Z
dc.date.accessioned2022-10-06T12:26:37Z
dc.date.available2010-09-09
dc.date.available2014-12-17T14:54:55Z
dc.date.available2022-10-06T12:26:37Z
dc.date.created2010-09-09
dc.date.created2014-12-17T14:54:55Z
dc.date.issued2010-03-16
dc.identifierRODRIGUES, Marconi Câmara. Identificação fuzzy-multimodelos para sistemas não lineares. 2010. 99 f. Tese (Doutorado em Automação e Sistemas; Engenharia de Computação; Telecomunicações) - Universidade Federal do Rio Grande do Norte, Natal, 2010.
dc.identifierhttps://repositorio.ufrn.br/jspui/handle/123456789/15143
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3952304
dc.description.abstractThis paper presents a new multi-model technique of dentification in ANFIS for nonlinear systems. In this technique, the structure used is of the fuzzy Takagi-Sugeno of which the consequences are local linear models that represent the system of different points of operation and the precursors are membership functions whose adjustments are realized by the learning phase of the neuro-fuzzy ANFIS technique. The models that represent the system at different points of the operation can be found with linearization techniques like, for example, the Least Squares method that is robust against sounds and of simple application. The fuzzy system is responsible for informing the proportion of each model that should be utilized, using the membership functions. The membership functions can be adjusted by ANFIS with the use of neural network algorithms, like the back propagation error type, in such a way that the models found for each area are correctly interpolated and define an action of each model for possible entries into the system. In multi-models, the definition of action of models is known as metrics and, since this paper is based on ANFIS, it shall be denominated in ANFIS metrics. This way, ANFIS metrics is utilized to interpolate various models, composing a system to be identified. Differing from the traditional ANFIS, the created technique necessarily represents the system in various well defined regions by unaltered models whose pondered activation as per the membership functions. The selection of regions for the application of the Least Squares method is realized manually from the graphic analysis of the system behavior or from the physical characteristics of the plant. This selection serves as a base to initiate the linear model defining technique and generating the initial configuration of the membership functions. The experiments are conducted in a teaching tank, with multiple sections, designed and created to show the characteristics of the technique. The results from this tank illustrate the performance reached by the technique in task of identifying, utilizing configurations of ANFIS, comparing the developed technique with various models of simple metrics and comparing with the NNARX technique, also adapted to identification
dc.publisherUniversidade Federal do Rio Grande do Norte
dc.publisherBR
dc.publisherUFRN
dc.publisherPrograma de Pós-Graduação em Engenharia Elétrica
dc.publisherAutomação e Sistemas; Engenharia de Computação; Telecomunicações
dc.rightsAcesso Aberto
dc.subjectIdentificação
dc.subjectMúltiplos modelos
dc.subjectSistemas fuzzy T-S
dc.subjectNeuro-fuzzy
dc.subjectANFIS
dc.subjectNão linear
dc.subjectIdentification
dc.subjectMultiple models
dc.subjectT-S Fuzzy systems
dc.subjectNeuro-fuzzy
dc.subjectANFIS
dc.subjectNonlinear
dc.titleIdentificação fuzzy-multimodelos para sistemas não lineares
dc.typedoctoralThesis


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