Identificação de Modelos de Hammerstein Multivariáveis com Não Linearidades Estáticas Fortes

Abstract

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.