Identification of nonlinear systems based on adaptive fuzzy systems embedding Quasi-ARMAX model

Takayuki Furuzuki, Kousuke Kumamaru

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper addresses the issues related to identification of nonlinear systems based on Adaptive Fuzzy Systems (AFSs) Embedding Quasi-ARMAX Model. A general nonlinear system can be represented as a Quasi-ARMAX form, in which the parameters āi and b̄i contain two parts: linear part and nonlinear part. By introducing and Adaptive Fuzzy System (AFS) to the nonlinear part of each parameter, we propose an AFSs Embedding Quasi-ARMAX Model for identification of nonlinear systems. Since the Quasi-ARMAX model consists of a combination of AFSs and ARMAX model, a priori information about system structure besides input-output data can be incorporated into the identification. Simulation results show that the proposed Quasi-ARMAX model has better performance than Neural Networks (NN) model.

Original languageEnglish
Title of host publicationProceedings of the SICE Annual Conference
Place of PublicationTokyo, Japan
PublisherSociety of Instrument and Control Engineers (SICE)
Pages1211-1216
Number of pages6
Publication statusPublished - 1995
Externally publishedYes
EventProceedings of the 34th SICE Annual Conference - Hokkaido, Jpn
Duration: 1995 Jul 261995 Jul 28

Other

OtherProceedings of the 34th SICE Annual Conference
CityHokkaido, Jpn
Period95/7/2695/7/28

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Furuzuki, T., & Kumamaru, K. (1995). Identification of nonlinear systems based on adaptive fuzzy systems embedding Quasi-ARMAX model. In Proceedings of the SICE Annual Conference (pp. 1211-1216). Society of Instrument and Control Engineers (SICE).