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A Comparative Study of two Approaches for Data-Driven Design of Evolving Fuzzy Systems: eTS and FLEXFIS

Research output: Contribution to journalJournal article

Published

Journal publication date02/2008
JournalInternational Journal of General Systems
Journal number1
Volume37
Number of pages23
Pages45-67
Original languageEnglish

Abstract

In this paper, two approaches for the incremental data-driven learning of one of the most effective fuzzy model, namely of so-called Takagi-Sugeno type, are compared. The algorithms that realize these approaches include not only adaptation of linear parameters in fuzzy systems appearing in the rule consequents, but also incremental learning and evolution of premise parameters appearing in the membership functions (i.e. fuzzy sets) in sample mode together with a rule learning strategy. In this sense the proposed methods are applicable for fast model training tasks in various industrial processes, whenever there is a demand of online system identification in order to apply models representing nonlinear system behaviors to system monitoring, online fault detection or open-loop control. An evaluation of the incremental learning algorithms are included at the end of the paper, where a comparison between conventional batch modelling methods for fuzzy systems and the incremental learning methods demonstrated in this paper is made with respect to model qualities and computation time. This evaluation is based on high dimensional data coming from an industrial measuring process as well as from a known source on the Internet, which underlines the usage of the new method for fast online identification tasks. (c) Taylor and Fransis Group (to appear in International Journal on General Systems)

Bibliographic note

The final, definitive version of this article has been published in the Journal, International Journal of General Systems, 37 (1), 2008, © Informa Plc