A Sliding Mode Control Learning of Interval Type-2 Intuitionistic Fuzzy Logic for Non-Linear System Prediction

— Many learning algorithms such as gradient descent, extended Kalman filter, decoupledextended Kalman filter and hybrid approaches have successfully been employed to optimizeintuitionistic fuzzy systems of type-1 and type-2. In this paper, a sliding mode control algorithm forthe optimization of interval type-2 intuitionistic fuzzy logic system parameters is proposed for thefirst time. The proposed model adopts the Takagi-Sugeno-type inference. The learning model isdeveloped and the adaptation for the parameters are derived. The proposed model is applied to nonlinear dynamic prediction problems. Experimental results show that interval type-2 intuitionisticfuzzy logic system with sliding mode control learning outperforms its type-1 counterpart and someexisting models in the literature while competing favorably with other models on the selectedapplication domains with less running time.