Parameter Prediction with Novel Enhanced Wagner Hagras Interval Type-3 Takagi–Sugeno–Kang Fuzzy System with Type-1 Non-Singleton Inputs
Gerardo Armando Hernández Castorena,
Gerardo Maximiliano Méndez,
Ismael López-Juárez,
María Aracelia Alcorta García,
Dulce Citlalli Martinez-Peon,
Pascual Noradino Montes-Dorantes
Affiliations
Gerardo Armando Hernández Castorena
Facultad de Ingeniería Civil, Universidad Autónoma de Nuevo León, San Nicolás de los Garza C.P. 66455, NL, Mexico
Gerardo Maximiliano Méndez
Departamento de Ingeniería Eléctrica y Electrónica, Instituto Tecnológico de Nuevo León, TecNM, Av. Eloy Cavazos 2001, Cd. Guadalupe CP 67170, NL, Mexico
Facultad de Ciencias Físico Matemáticas, Universidad Autónoma de Nuevo León, San Nicolás de los Garza C.P. 66455, NL, Mexico
Dulce Citlalli Martinez-Peon
Departamento de Ingeniería Eléctrica y Electrónica, Instituto Tecnológico de Nuevo León, TecNM, Av. Eloy Cavazos 2001, Cd. Guadalupe CP 67170, NL, 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 CP 25280, CH, Mexico
This paper presents the novel enhanced Wagner–Hagras interval type-3 Takagi–Sugeno–Kang fuzzy logic system with type-1 non-singleton inputs (EWH IT3 TSK NSFLS-1) that uses the backpropagation (BP) algorithm to train the antecedent and consequent parameters. The proposed methodology dynamically changes the parameters of only the alpha-0 level, minimizing some criterion functions as the current information becomes available for each alpha-k level. The novel fuzzy system was applied in two industrial processes and several fuzzy models were used to make comparisons. The experiments demonstrated that the proposed fuzzy system has a superior ability to predict the critical variables of the tested processes with lower prediction errors than those produced by the benchmark fuzzy systems.
