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Simpl_eTS: a simplified method for learning evolving Takagi-Sugeno fuzzy models

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2 Author(s)
Angelov, P. ; Dept. of Commun. Syst., Lancaster Univ. ; Filev, D.

This paper deals with a simplified version of the evolving Takagi-Sugeno (eTS) learning algorithm - a computationally efficient procedure for on-line learning TS type fuzzy models. It combines the concept of the scatter as a measure of data density and summarization ability of the TS rules, the use of Cauchy type antecedent membership functions, an aging indicator characterizing the stationarity of the rules, and a recursive least square algorithm to dynamically learn the structure and parameters of the eTS model

Published in:

Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on

Date of Conference:

25-25 May 2005