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

Abstract

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> 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. </para>