T-S Fuzzy Model Identification Based on Chaos Optimization
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Fuzzy-CCM: A context-sensitive approach to fuzzy modeling
International Journal of Hybrid Intelligent Systems - Hybrid Fuzzy Models
Engineering Applications of Artificial Intelligence
An improved Takagi-Sugeno fuzzy model with multidimensional fuzzy sets
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - FUZZYSS’2009
Data and feature reduction in fuzzy modeling through particle swarm optimization
Applied Computational Intelligence and Soft Computing
Hybrid-fuzzy modeling and identification
Applied Soft Computing
Hi-index | 0.00 |
Fuzzy models, especially Takagi-Sugeno (T-S) fuzzy models, have received particular attention in the area of nonlinear modeling due to their capability to approximate any nonlinear behavior. Based only on measured data without any prior knowledge, there is no systematic way to obtain a T-S fuzzy model with a simple structure and sufficient accuracy. The main idea discussed in this paper is to reduce the complexity of T-S fuzzy models by estimating an optimal number of fuzzy rules and selecting relevant inputs as antecedent variables independently of the selection of consequent regressors. A systematic procedure is proposed here and illustrated on static and dynamical nonlinear systems.