Interval type-2 non-singleton type-2 Takagi-Sugeno-Kang fuzzy logic systems using the hybrid learning mechanism recursive-least-square and back-propagation methods

Abstract

This article presents a novel learning methodology based on the hybrid mechanism for training an interval type-2 non-singleton type-2 Takagi-Sugeno-Kang fuzzy logic systems (FLS). Using input-output data pairs during the forward pass of the training and prediction processes, the interval type-2 non-singleton type-2 TSK FLS the consequent parameters were tuned by using the recursive least squares (RLS) method. In the backward pass, the antecedent parameters were tuned by using the back-propagation (BP) method. As reported in the literature, the performance indexes of these hybrid models have proved to be better than the individual training mechanism when used alone. The proposed hybrid methodology was tested thru the modeling and prediction of the steel strip temperature at the descaler box entry as rolled in an industrial hot strip mill. Results show that the proposed method compensates better for uncertain measurements than previous type-2 Takagi-Sugeno-Kang using non-hybrid or only back propagation learning mechanisms.