NDLI: Two approaches to data-driven design of evolving fuzzy systems: eTS and FLEXFIS

Content Provider	IEEE Xplore Digital Library
Author	Angelov, P. Lughofer, E. Klement, E.P.
Copyright Year	2005
Description	Author affiliation: Dept. of Commun. Syst., Lancaster Univ., UK (Angelov, P.)
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 realise 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.
Starting Page	31
Ending Page	36
File Size	1741219
Page Count	6
File Format	PDF
ISBN	078039187X
DOI	10.1109/NAFIPS.2005.1548502
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-06-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy systems Industrial training Takagi-Sugeno model Fuzzy sets Electrical equipment industry Industrial control System identification Nonlinear systems Monitoring Fault detection online identification Incremental learning adaptation of parameters evolving Takagi-Sugeno fuzzy systems rule learning
Content Type	Text
Resource Type	Article

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