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Takagi--Sugeno--Kang Fuzzy Classifiers for a Special Class of Time-Varying Systems

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4 Author(s)
Mikut, R. ; Inst. of Appl. Comput. Sci., Forschungszentrum Karlsruhe GmbH, Karlsruhe ; Burmeister, O. ; Groll, L. ; Reischl, M.

This paper proposes new design strategies for Takagi-Sugeno-Kang classifiers to solve a special class of time-varying classification problems with known or estimated trigger events. The resulting classifiers have lower classification errors than time-invariant classifiers, as well as a lower computational effort and a better interpretability than other multiple classifiers with a time-varying fusion. The strategies are applied to several benchmark datasets and to a real-world application to design a brain-machine interface.

Published in:

Fuzzy Systems, IEEE Transactions on  (Volume:16 ,  Issue: 4 )

Date of Publication:

Aug. 2008

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