Title :
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.
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;
Conference_Titel :
Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on
Conference_Location :
Reno, NV
Print_ISBN :
0-7803-9159-4
DOI :
10.1109/FUZZY.2005.1452543
