A differential evolution based adaptive neural Type-2 Fuzzy inference system for classification of motor imagery EEG signals

This paper proposes a new classification algorithm which aims at predicting different states from an incoming non-stationary signal. To overcome the failure of standard classifiers at generalizing the patterns for such signals, we have proposed an Interval Type-2 Fuzzy based Adaptive neural fuzzy Inference System (ANFIS). Through the introduction IT2F system, we have aimed at improving the uncertainty management of the fuzzy inference system. Besides that using DE in forward and backward pass and improving the forward pass function we have improved the parameter update on wide range of nodal functions without any quadratic approximation in forward pass. The proposed algorithm is tested on a standard electroencephalography (EEG) dataset and it is noted that the proposed algorithm performs better than other standard classifiers including the classical ANFIS algorithm.