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

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

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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

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11 Jan 2008 11:42
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21 Nov 2022 14:42