G.M. Méndez, L.A. Leduc, and M. de los Angeles Hernandez M. (Mexico)
Type-2 fuzzy logic systems, uncertainty, temperature modelling and prediction
This article presents a novel training algorithm for interval type-1 non-singleton type-2 TSK fuzzy logic system (FLS). Using input-output data pairs during the forward pass of the training process, the interval type-1 non-singleton type-2 TSK FLS output is calculated and the consequent parameters are estimated by back propagation (BP) method. In the backward pass, the error propagates backward, and the antecedent parameters are estimated also by back-propagation. The proposed interval type-1 non-singleton type-2 TSK FLS system was used to construct a 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 finishing Scale Breaker (SB) entry zone, being able to compensate for uncertain measurements that first-order interval singleton type-2 TSK FLS can not do
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