Adaptive Fuzzy Modeling For A Large-Scale Nonlinear System

DOI

10.2991/jcis.2006.75How to use a DOI?

Keywords

Nonlinear system modeling, principal component analysis, Bayesian classification, Takagi-Sugeno fuzzy method.

Abstract

A data-driven Takagi-Sugeno (TS) fuzzy model is developed for modeling a real plant with the dependent inputs, the nonlinear and the time-varying input-output relation. The collinearity of inputs can be eliminated through the principal component analysis (PCA). The TS model split the operating region into a collection of IF-THEN rules. For each rule, the premise is generated from clustering the compressed input data and the consequence is represented as a linear model. A post-update algorithm for model parameters is also proposed to accommodate the time-varying nature. Effectiveness of the proposed model is demonstrated using real plant data from a polyethylene process.

Copyright

© 2006, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jialin Liu
PY  - 2006/10
DA  - 2006/10
TI  - Adaptive Fuzzy Modeling For A Large-Scale Nonlinear System
BT  - Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2006.75
DO  - 10.2991/jcis.2006.75
ID  - Liu2006/10
ER  -