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Parameter Prediction with Novel Enhanced Wagner Hagras Interval Type-3 Takagi–Sugeno–Kang Fuzzy System with Type-1 Non-Singleton Inputs

Author

Listed:
  • 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)

  • Ismael López-Juárez

    (Robotics and Advanced Manufacturing Department, CINVESTAV, Ramos Arizpe 25900, CH, Mexico)

  • María Aracelia Alcorta García

    (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)

Abstract

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.

Suggested Citation

  • Gerardo Armando Hernández Castorena & Gerardo Maximiliano Méndez & Ismael López-Juárez & María Aracelia Alcorta García & Dulce Citlalli Martinez-Peon & Pascual Noradino Montes-Dorantes, 2024. "Parameter Prediction with Novel Enhanced Wagner Hagras Interval Type-3 Takagi–Sugeno–Kang Fuzzy System with Type-1 Non-Singleton Inputs," Mathematics, MDPI, vol. 12(13), pages 1-39, June.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:13:p:1976-:d:1422643
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    References listed on IDEAS

    as
    1. Oscar Castillo & Fevrier Valdez & Cinthia Peraza & Jin Hee Yoon & Zong Woo Geem, 2021. "High-Speed Interval Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Harmony Search for Optimal Design of Fuzzy Controllers," Mathematics, MDPI, vol. 9(7), pages 1-18, April.
    2. Man-Wen Tian & Yassine Bouteraa & Khalid A. Alattas & Shu-Rong Yan & Abdullah K. Alanazi & Ardashir Mohammadzadeh & Saleh Mobayen & Hiroki Sayama, 2022. "A Type-3 Fuzzy Approach for Stabilization and Synchronization of Chaotic Systems: Applicable for Financial and Physical Chaotic Systems," Complexity, Hindawi, vol. 2022, pages 1-17, June.
    3. Acosta, Hernando & Wu, Dongrui & Forrest, Barrie M., 2010. "Fuzzy experts on recreational vessels, a risk modelling approach for marine invasions," Ecological Modelling, Elsevier, vol. 221(5), pages 850-863.
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