Special Issue: Neuro-Fuzzy Systems; Guest Editors: Leszek Rutkowski, M. J. Er and Janek Żurada — Short ContributionsNo Access

AUTOMATED NONLINEAR SYSTEM MODELING WITH MULTIPLE FUZZY NEURAL NETWORKS AND KERNEL SMOOTHING

WEN YU

Departamento de Control Automatico, CINVESTAV-IPN, Av.IPN 2508, México D.F., 07360, México

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and

XIAOOU LI

Departamento de Computación, CINVESTAV-IPN, Av.IPN 2508, México D.F., 07360, México

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https://doi.org/10.1142/S0129065710002516Cited by:37 (Source: Crossref)

Abstract

This paper, presents a novel identification approach using fuzzy neural networks. It focuses on structure and parameters uncertainties which have been widely explored in the literatures. The main contribution of this paper is that an integrated analytic framework is proposed for automated structure selection and parameter identification. A kernel smoothing technique is used to generate a model structure automatically in a fixed time interval. To cope with structural change, a hysteresis strategy is proposed to guarantee finite times switching and desired performance.

Keywords:

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