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

Jinglu Hu, Kousuke Kumamaru

Research output: Contribution to conferencePaper

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
Pages1211-1216
Number of pages6
Publication statusPublished - 1995 Dec 1
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

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Identification of nonlinear systems based on adaptive fuzzy systems embedding Quasi-ARMAX model'. Together they form a unique fingerprint.

  • Cite this

    Hu, J., & Kumamaru, K. (1995). Identification of nonlinear systems based on adaptive fuzzy systems embedding Quasi-ARMAX model. 1211-1216. Paper presented at Proceedings of the 34th SICE Annual Conference, Hokkaido, Jpn, .