On-Line T-S Fuzzy Model Identification with Growing and Pruning Rules

Authors:
Longtao Liao;Shaoyuan Li
Affiliations:
Department of Automation, Shanghai Jiao Tong University, 200240 Shanghai, China;Department of Automation, Shanghai Jiao Tong University, 200240 Shanghai, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

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Abstract

This paper focuses on seeking an appropriate number of rules for a T-S inference system. A growing and pruning strategy in neural network is employed, which relates one fuzzy rule's contribution to the modeling accuracy by a statistic criterion, such that fuzzy rules is added/removed, whereas all the parameters can learn using EKF, both absolutely on-line and with small computation. A simulation for nonlinear system identification illustrates the good performance.