Takagi--Sugeno--Kang Fuzzy Classifiers for a Special Class of Time-Varying Systems
Ralf Mikut, Ole Burmeister, Lutz Gröll, Markus Reischl · IEEE Transactions on Fuzzy Systems · 2008
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.