Identificação de modelos de Hammerstein multivariáveis com não-lineraridades estáticas fortes

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dc.creatorLuís Henrique dos Santos
dc.creatorRodrigo Augusto Ricco
dc.creatorBruno Otávio Soares Teixeira
dc.date.accessioned2025-06-03T14:57:34Z
dc.date.accessioned2025-09-09T00:32:11Z
dc.date.available2025-06-03T14:57:34Z
dc.date.issued2023
dc.identifier.doihttps://doi.org/10.20906/SBAI-SBSE-2023/4071
dc.identifier.urihttps://hdl.handle.net/1843/82745
dc.languagepor
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofSimpósio Brasileiro de Automação Inteligente SBAI / Simpósio Brasileiro de Sistemas Elétricos SBSE, 2023
dc.rightsAcesso Aberto
dc.subjectSistemas difusos
dc.subject.otherSystem identification, State-space modeling, Hard nonlinearities, Hammerstein model, Neuro-Fuzzy Systems
dc.titleIdentificação de modelos de Hammerstein multivariáveis com não-lineraridades estáticas fortes
dc.typeArtigo de evento
local.description.resumoThis article investigates the identification of interconnected block models with hard input nonlinearities. The cascated static nonlinear function followed by a linear dynamic representation is named Hammerstein model. The static nonlinearity is portrayed by a neural network that is simple and has accurate tuning capability, and the dynamic block, is represented by a state-space model that simplifies the extension to the multivariable case. Taking these characteristics into account, an approach was developed to identify a Hammerstein multivariable Neuro-Fuzzy model through a noniterative procedure associated with subspace identification methods. The functionality of the proposal was verified by simulation, yielding improved performance compared to the case of polynomial static nonlinear curve.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.initialsUFMG
local.url.externahttps://www.sba.org.br/open_journal_systems/index.php/sbai/article/view/4071

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