HYBRID LEARNING ALGORITHM FOR INTERVAL TYPE-2 FUZZY LOGIC SYSTEMS

Abstract

A new learning methodology based on a hybrid algorithm for interval type-2 fuzzy logic systems (FLS) parameter estimation is presented. The new proposal consists of recursive least-squares (RLS) with back-propagation (BP) and square-root filter (REFIL) with BP combinations. A system with the proposed methodology was simulated to test its capability for output surface temperature prediction of a transfer bar at a hot strip mill (HSM) scale breaker (SB) entry zone for three different types of coils. The inputs of the system are the surface temperature of the transfer bar and the traveling time required to reach the SB entry zone. The inputs are modelled as interval singleton, type-1, or type-2 fuzzy sets depending on whether the noise was taken into account or not. The following combinations were simulated: (a) interval singleton type-2 FLS (type-2 SFLS), (b) interval type-1 non-singleton type-2 FLS (type-2 NSFLS-1) and (c) interval type-2 non-singleton type-2 FLS (type-2 NSFLS-2), where the bold face terms indicate the kind of input. Modelling results show the feasibility of the proposed methodology to be implemented into an intelligent process controller for the HSM industry.