Modeling of a Hot Strip Mill Temperature using Hybrid Learning Interval Type-1 and Type-2 Non-singleton Type-2 FLSs

M. Méndez, A. Cavazos, L. Leduc, and R. Soto (Mexico)


Interval type2, stationary noise, nonstationary noise, temperature prediction, hot strip mill, leastsquared, back propagation, squareroot filter, Mamdani ANFIS type2.


This paper presents results of a research on two proposed hybrid learning algorithms: back-propagation (BP) recursive least-squared (RLS) and back-propagation (BP) square-root filter (REFIL) for interval singleton type-2 Fuzzy Logic System (FLS) (type-2 SFLS) parameters estimation [1]. The algorithms are extended to interval type-1 non-singleton type-2 FLS (type-2 NSFLS-1) and interval type-2 non-singleton type-2 FLS (type-2 NSFLS 2). The proposed algorithms are evaluated in a Hot Strip Mill (HSM) Scale Breaker (SB) entry surface temperature prediction application. A comparative result shows the advantage of hybrid learning methods over that with only BP [2].

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