Evolving Takagi-Sugeno Fuzzy Models for Data Mining



We are studying and experiencing approaches for adaptive on-line learning of fuzzy rules and their application for prediction problems in the context of data mining. Takagi-Sugeno (TS) fuzzy models are used for knowledge representation and the mechanism for on-line learning is based on algorithms that recursively update the model structure and parameters by combining supervised and unsupervised (hybrid) learning. The structure and parameters of the model continually evolve by adding new rules and by modifying existing rules and parameters during the operation of the system. The work is based on developments from the original contributions of Stephen Chiu, Plamen Angelov and Nikola Kasabov.


Takagi-Sugeno fuzzy models, fuzzy clustering, rule-base adaptation, on-line learning


On-line Learning Algorithms


SoftComplex'03, June 2003

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Year 2009 : 2 citations

 A Decade of Kasabovapos;s Evolving Connectionist Systems: A Review

 Watts, M.J. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on Volume 39, Issue 3, May 2009 Page(s):253 - 269