A nonlinear system identification approach based on neuro-fuzzy networks

Author

Li, Ying ; Zhao, Xueyun ; Jiao, Licheng

Author_Institution

Key Lab. for Signal Process., Xidian Univ., Xi´´an, China

Volume

3

fYear

2000

fDate

2000

Firstpage

1594

Abstract

This paper presents a nonlinear system identification approach based on neuro-fuzzy networks. This method consists of two main step: 1) concerns with the structure identification or learning, which includes selection of input variables and determination of the number of fuzzy rules and initial terms for membership functions; and 2) deals with parameter identification or learning. Its task is to adjust the weights of the neuro-fuzzy network, i.e., the antecedent and consequent parameters of rules, so that the error between the desired and real output is minimum. The effectiveness of the proposed technique is confirmed by simulation results

Keywords

fuzzy neural nets; identification; learning (artificial intelligence); nonlinear systems; fuzzy neural networks; identification; membership functions; nonlinear system; supervised learning; Input variables; Linear systems; Linearity; Nonlinear systems; Parameter estimation; Partitioning algorithms; Signal processing; Signal processing algorithms; System identification; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing Proceedings, 2000. WCCC-ICSP 2000. 5th International Conference on

Conference_Location

Beijing

Print_ISBN

0-7803-5747-7

Type

conf

DOI

10.1109/ICOSP.2000.893405

Filename

893405