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

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… (More)
DOI: 10.1109/FUZZY.2005.1452543

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@article{Angelov2005Simpl_eTSAS, title={Simpl_eTS: a simplified method for learning evolving Takagi-Sugeno fuzzy models}, author={Plamen P. Angelov and Dimitar Filev}, journal={The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05.}, year={2005}, pages={1068-1073} }