Affiliations: Department of Electromechanical and Electronics
Engineering, Instituto Tecnológico de Nuevo León, Cd. Guadalupe,
Nuevo León, CP 67170, México | Facultad de Ingeniería Mecánica
Eléctrica, Universidad Autónoma de Nuevo León | Center for Intelligent Systems, Tecnológico de
Monterrey, Campus Monterrey, Monterrey, Nuevo León, CP. 64849,
México | Department of Process Engineering, Hylsa, S.A. de
C.V., San Nicolas de los Garza, Nuevo León, CP 66452,
México
Note: [] Address for correspondence: Apdo. Postal 115-F, Suc. Ciudad
Universitaria, 66450 San Nicolas de los Garza, N.L., Mexico. Tel.: +52 81 2282
9284; Fax: +52 81 1052 3550; E-mail: acavazos@fime.uanl.mx
Abstract: In Hot Strip Mills, on-line estimation of rolling variables is of
crucial importance in order to Set-Up the Finishing Mill, i.e. setting initial
working references for the in-bar regulators, and hence fulfilling quality
requirements. This paper presents the experimental results of the application
of type-2 fuzzy logic systems for scale breaker entry temperature prediction in
a real hot strip mill. Since in the literature only back-propagation has been
proposed for type-2 fuzzy logic systems, a hybrid learning algorithm has been
developed. Such algorithm is also presented. The algorithm uses
back-propagation with recursive least-squares and back propagation with
square-root filter methods. The systems were tested for three types of inputs:
a) interval singleton b) interval type-1 non-singleton, c) interval type-2
non-singleton. The experiments were carried out for three different types of
coils. Experimental results show the feasibility of the systems developed here
for scale breaker entry temperature prediction. Comparison with type-1 fuzzy
logic systems shows the hybrid learning type-2 fuzzy logic systems improve
performance in scale breaker entry temperature prediction under the tested
condition.
Keywords: Type-2 fuzzy inference systems, type-2 neuro-fuzzy systems, hybrid learning, uncertain rule-based fuzzy logic systems, temperature modeling and prediction
