Computational intelligence applied to signal processing: a proposal for fuzzy neural identification

Author

Bottura, Celso Pascoli ; De Oliveira Serra, Ginalber Luiz

Author_Institution

Control & Intelligent Syst. Lab., Campinas State Univ.

fYear

2004

fDate

Sept. 29 2004-Oct. 1 2004

Firstpage

113

Lastpage

122

Abstract

In this study an approach to fuzzy neural identification of MIMO discrete-time nonlinear dynamical systems is proposed. Based on the Takagi-Sugeno (TS) fuzzy neural network, off-line and on-line schemes are formulated as a NARX (nonlinear autoregressive with exogenous input) fuzzy neural model from samples of a nonlinear dynamical system where the consequent parameters are modified by an adaptive WIV (weighted instrumental variable) algorithm based on the numerically robust orthogonal householder transformation

Keywords

MIMO systems; autoregressive processes; discrete time systems; fuzzy neural nets; identification; nonlinear dynamical systems; numerical stability; signal processing; transforms; MIMO discrete-time nonlinear dynamical systems; Takagi-Sugeno fuzzy neural network; adaptive weighted instrumental variable algorithm; computational intelligence; fuzzy neural identification; nonlinear autoregressive with exogenous input; orthogonal householder transformation; signal processing; Computational intelligence; Fuzzy neural networks; Fuzzy systems; Instruments; MIMO; Nonlinear dynamical systems; Proposals; Signal processing; Signal processing algorithms; Takagi-Sugeno model;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning for Signal Processing, 2004. Proceedings of the 2004 14th IEEE Signal Processing Society Workshop

Conference_Location

Sao Luis

ISSN

1551-2541

Print_ISBN

0-7803-8608-4

Type

conf

DOI

10.1109/MLSP.2004.1422965

Filename

1422965