Paper Info
Title
Structure optimization of Takagi-Sugeno fuzzy models
Abstract
A new approach for nonlinear system identification based on Takagi-Sugeno fuzzy models is presented. The premise structure and membership functions are optimized by the LOLIMOT (local linear model tree) algorithm, see [1]. This method is extended by a subset selection technique which automatically determines the structure of the local linear models in the rule consequents. This allows to select the significant input variables for static models and additionally the determination of the dynamic orders and dead times for dynamic models. The utilized subset selection technique is the orthogonal least-squares (OLS) algorithm. It exploits the linear regression structure of the problem and thus is very fast. The applicability of the proposed approach is illustrated by the identification of a transport delay process which has operating point dependent time constants and dead times.
Year
DOI
Venue
1998
10.1142/S0218488598000148
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Keywords
Field
DocType
takagi-sugeno fuzzy model,structure optimization,nonlinear
Mathematical optimization,Nonlinear system,Linear model,Operating point,Fuzzy logic,Nonlinear system identification,Artificial intelligence,Dynamic models,Time constant,Machine learning,Mathematics,Linear regression
Journal
Volume
Issue
ISSN
6
2
0218-4885
Citations 
PageRank 
References 
0
0.34
0
Authors
1
Name
Order
Citations
PageRank
Oliver Nelles19917.27