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

Author

Angelov, Plamen ; Filev, Dimitar

Author_Institution

Dept. of Commun. Syst., Lancaster Univ.

fYear

2005

fDate

25-25 May 2005

Firstpage

1068

Lastpage

1073

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

Keywords

fuzzy set theory; fuzzy systems; learning (artificial intelligence); least squares approximations; recursive functions; Cauchy type antecedent membership functions; Simpl_eTS; data density; data summarization; learning evolving Takagi-Sugeno fuzzy models; online learning; recursive least square algorithm; rule stationarity; structure dynamic learning; Aging; Clustering algorithms; Density measurement; Fuzzy sets; Fuzzy systems; Least squares methods; Scattering parameters; Signal processing algorithms; Takagi-Sugeno model; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

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

Conference_Location

Reno, NV

Print_ISBN

0-7803-9159-4

Type

conf

DOI

10.1109/FUZZY.2005.1452543

Filename

1452543