The Takagi-Sugeno Fuzzy Model Identification Method of Parameter Varying Systems

Authors:
Xie Keming;T. Y. Lin;Jianwei Zhang
Affiliations:
-;-;-
Venue:
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Year:
1998

Citing 3
Cited 1

Structure identification of fuzzy model

Fuzzy Sets and Systems
Successive identification of a fuzzy model and its applications to prediction of a complex system

Fuzzy Sets and Systems
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence

Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence

Theoretical and fuzzy modelling of a pharmaceutical batch reactor

Mathematical and Computer Modelling: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents the TS model identification method by which a great number of systems whose parameters vary dramatically with working states can be identified via Fuzzy Neural Networks (FNN). The suggested method could overcome the drawbacks of traditional linear system identification methods which are only effective under certain narrow working states and provide global dynamic description based on which further control of such systems may be carried out. Simulation results of a second-order parameter varying system demonstrate the effectiveness of the method.