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 and Pascual Noradino Montes-Dorantes ()
Additional contact information
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

Mathematics, 2024, vol. 12, issue 13, 1-39

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.

Keywords: backpropagation algorithm; gradient descent algorithm; GMAW; HSM; interval type-3 fuzzy systems; robotic gas metal arc welding; type-1 non-singleton inputs (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/13/1976/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/13/1976/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:13:p:1976-:d:1422643

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().