Identificação de modelos de Hammerstein multivariáveis com não-lineraridades estáticas fortes
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Universidade Federal de Minas Gerais
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This 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.
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System identification, State-space modeling, Hard nonlinearities, Hammerstein model, Neuro-Fuzzy Systems
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https://www.sba.org.br/open_journal_systems/index.php/sbai/article/view/4071