Angelov, Plamen and Filev, Dimitar
(2005)
Simpl_eTS: a simplified method for learning evolving Takagi-Sugeno fuzzy models.
In:
Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on.
IEEE, Reno, Las vegas, USA, pp. 1068-1073.
ISBN 0-7803-9159-4
Full text not available from this repository.
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
Item Type:
Contribution in Book/Report/Proceedings
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Uncontrolled Keywords:
/dk/atira/pure/researchoutput/libraryofcongress/qa75
Subjects:
?? COMPUTING, COMMUNICATIONS AND ICTQA75 ELECTRONIC COMPUTERS. COMPUTER SCIENCE ??
Deposited On:
11 Jan 2008 11:42
Last Modified:
21 Sep 2023 03:43