Detalhes bibliográficos
Ano de defesa: |
2019 |
Autor(a) principal: |
EVANGELISTA, Anderson Pablo Freitas
|
Orientador(a): |
SERRA, Ginalber Luiz de Oliveira
|
Banca de defesa: |
SERRA, Ginalber Luiz de Oliveira
,
CORTES, Omar Andres Carmona
,
SOUZA, Francisco das Chagas de
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Tipo de documento: |
Dissertação
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Tipo de acesso: |
Acesso aberto |
Idioma: |
por |
Instituição de defesa: |
Universidade Federal do Maranhão
|
Programa de Pós-Graduação: |
PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET
|
Departamento: |
DEPARTAMENTO DE ENGENHARIA DA ELETRICIDADE/CCET
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País: |
Brasil
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Palavras-chave em Português: |
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Área do conhecimento CNPq: |
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Link de acesso: |
https://tedebc.ufma.br/jspui/handle/tede/3025
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Resumo: |
In this dissertation, a methodology for the identification of multivariable dynamic systems based on state-space neural-fuzzy model with evolving type-2 interval inference is proposed. An evolving learning algorithm for interval type-2 neural-fuzzy inference system is presented, where the combination of a fuzzy clustering method based on participatory learning and Extend Kalman filter is used for estimating the type-2 membership functions (shape and footprint of uncertainty). For estimating the consequent proposition, a fuzzy state-space identification algorithm is proposed, where the Markov parameters are recursively estimated, which are used to computing the state-space parameters incrementally. The efficiency and applicability of the proposed methodology are demonstrated through computational results (nonlinear SISO dynamic system, combined complex nonlinear dynamic system, time-varying nonlinear dynamic system) and experimental results (four-stage evaporator and helicopter with two degrees of freedom). |