The Enhanced Wagner–Hagras OLS–BP Hybrid Algorithm for Training IT3 NSFLS-1 for Temperature Prediction in HSM Processes

Gerardo Maximiliano Méndez (), Ismael López-Juárez, María Aracelia Alcorta García, Dulce Citlalli Martinez-Peon and Pascual Noradino Montes-Dorantes ()
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Gerardo Maximiliano Méndez: Departamento de Ingeniería Eléctrica y Electrónica, Instituto Tecnológico de Nuevo León-TecNM, Av. Eloy Cavazos 2001, Cd., Guadalupe 67170, Mexico
Ismael López-Juárez: CINVESTAV-IPN Saltillo, Robotics and Advanced Manufacturing Department, Ramos Arizpe 25900, Mexico
María Aracelia Alcorta García: Facultad de Ciencias Físico Matemáticas, Universidad Autónoma de Nuevo León, San Nicolás de los Garza 66455, Mexico
Dulce Citlalli Martinez-Peon: Departamento de Ingeniería Eléctrica y Electrónica, Instituto Tecnológico de Nuevo León-TecNM, Av. Eloy Cavazos 2001, Cd., Guadalupe 67170, Mexico
Pascual Noradino Montes-Dorantes: Departamento de Ciencias Económico-Administrativas, Departamento de Educación a Distancia, Instituto Tecnológico de Saltillo-TecNM, Blvd. Venustiano Carranza, Priv. Tecnológico 2400, Saltillo 25280, Mexico

Mathematics, 2023, vol. 11, issue 24, 1-33

Abstract: This paper presents (a) a novel hybrid learning method to train interval type-1 non-singleton type-3 fuzzy logic systems (IT3 NSFLS-1), (b) a novel method, named enhanced Wagner–Hagras (EWH) applied to IT3 NSFLS-1 fuzzy systems, which includes the level alpha 0 output to calculate the output y alpha using the average of the outputs y alpha k instead of their weighted average, and (c) the novel application of the proposed methodology to solve the problem of transfer bar surface temperature prediction in a hot strip mill. The development of the proposed methodology uses the orthogonal least square (OLS) method to train the consequent parameters and the backpropagation (BP) method to train the antecedent parameters. This methodology dynamically changes the parameters of only the level alpha 0, minimizing some criterion functions as new information becomes available to each level alpha k . The precursor sets are type-2 fuzzy sets, the consequent sets are fuzzy centroids, the inputs are type-1 non-singleton fuzzy numbers with uncertain standard deviations, and the secondary membership functions are modeled as two Gaussians with uncertain standard deviation and the same mean. Based on the firing set of the level alpha 0, the proposed methodology calculates each firing set of each level alpha k to dynamically construct and update the proposed EWH IT3 NSFLS-1 (OLS–BP) system. The proposed enhanced fuzzy system and the proposed hybrid learning algorithm were applied in a hot strip mill facility to predict the transfer bar surface temperature at the finishing mill entry zone using, as inputs, (1) the surface temperature measured by the pyrometer located at the roughing mill exit and (2) the time taken to translate the transfer bar from the exit of the roughing mill to the entry of the descale breaker of the finishing mill. Several fuzzy tools were used to make the benchmarking compositions: type-1 singleton fuzzy logic systems (T1 SFLS), type-1 adaptive network fuzzy inference systems (T1 ANFIS), type-1 radial basis function neural networks (T1 RBFNN), interval singleton type-2 fuzzy logic systems (IT2 SFLS), interval type-1 non-singleton type-2 fuzzy logic systems (IT2 NSFLS-1), type-2 ANFIS (IT2 ANFIS), IT2 RBFNN, general singleton type-2 fuzzy logic systems (GT2 SFLS), general type-1 non-singleton type-2 fuzzy logic systems (GT2 NSFLS-1), interval singleton type-3 fuzzy logic systems (IT3 SFLS), and interval type-1 non-singleton type-3 fuzzy systems (IT3 NSFLS-1). The experiments show that the proposed EWH IT3 NSFLS-1 (OLS–BP) system presented superior capability to learn the knowledge and to predict the surface temperature with the lower prediction error.

Keywords: interval type-3 fuzzy logic systems; hybrid learning; backpropagation method; orthogonal least square method; general type-2 fuzzy logic systems (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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