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

Research output: Contribution in Book/Report/ProceedingsPaper

Published

Publication date22/05/2005
Host publicationFuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on
PublisherIEEE
Pages1068-1073
Number of pages6
ISBN (Print)0-7803-9159-4
Original languageEnglish

Conference

ConferenceThe 2005 IEEE International Conference on Fuzzy Systems FUZZ-IEEE
CityReno, Las vegas, USA
Period22/05/0525/05/05

Conference

ConferenceThe 2005 IEEE International Conference on Fuzzy Systems FUZZ-IEEE
CityReno, Las vegas, USA
Period22/05/0525/05/05

Abstract

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. (c) IEEE Press

Bibliographic note

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