| Hi-index | 0.00 |
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.