On-line fuzzy modeling via clustering and support vector machines

Authors:
Wen Yu;Xiaoou Li
Affiliations:
Departamento de Control Automatico, CINVESTAV-IPN, Av.IPN 2508, México D.F. 07360, Mexico;Departamento de Computación, CINVESTAV-IPN, México D.F. 07360, Mexico
Venue:
Information Sciences: an International Journal
Year:
2008

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Abstract

In this paper, we propose a novel approach to identify unknown nonlinear systems with fuzzy rules and support vector machines. Our approach consists of four steps which are on-line clustering, structure identification, parameter identification and local model combination. The collected data are firstly clustered into several groups through an on-line clustering technique, then structure identification is performed on each group using support vector machines such that the fuzzy rules are automatically generated with the support vectors. Time-varying learning rates are applied to update the membership functions of the fuzzy rules. The modeling errors are proven to be robustly stable with bounded uncertainties by a Lyapunov method and an input-to-state stability technique. Comparisons with other related works are made through a real application of crude oil blending process. The results demonstrate that our approach has good accuracy, and this method is suitable for on-line fuzzy modeling.