Adaptive Fuzzy Modeling For A Large-Scale Nonlinear System
- 10.2991/jcis.2006.75How to use a DOI?
- Nonlinear system modeling, principal component analysis, Bayesian classification, Takagi-Sugeno fuzzy method.
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.
- © 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/).
Cite this article
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 -