First-order Interval Type-2 TSK Fuzzy Logic Systems Using a Hybrid Learning Algorithm

This article presents a new learning methodology based on a hybrid algorithm for interval type-2 TSK fuzzy logic systems (FLS). Using input-output data pairs during the forward pass of the training process, the interval type-2 TSK FLS output is calculated and the consequent parameters are estimated by recursive least-squares (RLS) method. In the backward pass, the error propagates backward, and the antecedent parameters are estimated by back-propagation (BP) method. The proposed hybrid methodology was used to construct an interval type-2 TSK fuzzy model capable of approximating the behaviour of the steel strip temperature as it is being rolled in an industrial Hot Strip Mill (HSM) and used to predict the transfer bar surface temperature at the finishing Scale Breaker (SB) entry zone. Comparative results show the advantage of the hybrid learning method RLS-BP over BP. Key-Words: Interval type-2 TKS fuzzy inference systems; Interval type-2 TSK neuro-fuzzy systems; Hybrid learning; Uncertain rule-based TKS fuzzy logic systems; Temperature modelling and prediction.