CISUC

On-line Evolution of Takagi-Sugeno Fuzzy Models

Authors

Plamen Angelov
José Victor
António Dourado
Dimitar Filev

Abstract

Evolving Takagi-Sugeno (eTS) fuzzy models and the method for their on-line identification has been recently introduced for both MISO and MIMO case. In this paper, the mechanism for rule-base evolution, one of the central points of the algorithm together with the recursive clustering and modified recursive least squares (RLS) estimation, is studied in detail. Different scenarios are considered for the rule base upgrade and modification. The radius of influence of each fuzzy rule is considered to be a vector instead of a scalar as in the original eTS approach, allowing different areas of the data space to be covered by each input variable. Simulation results using a well-known benchmark (Mackey-Glass chaotic time-series prediction) are presented. Copyright © 2004 IFAC

Keywords

evolving Takagi-Sugeno fuzzy models, rule-base evolution, recursive clustering, RLS algorithm.

Subject

Fuzzy Modelling

Conference

The 2nd IFAC Workshop on Advanced Fuzzy/Neural Control, AFNC'04, September 2004


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