IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i24p4933-d1298587.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Gerardo Maximiliano Méndez & Ismael López-Juárez & María Aracelia Alcorta García & Dulce Citlalli Martinez-Peon & Pascual Noradino Montes-Dorantes, 2023. "The Enhanced Wagner–Hagras OLS–BP Hybrid Algorithm for Training IT3 NSFLS-1 for Temperature Prediction in HSM Processes," Mathematics, MDPI, vol. 11(24), pages 1-33, December.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:24:p:4933-:d:1298587
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/24/4933/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/24/4933/
    Download Restriction: no
    ---><---

    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. Oscar Castillo & Patricia Melin, 2022. "Towards Interval Type-3 Intuitionistic Fuzzy Sets and Systems," Mathematics, MDPI, vol. 10(21), pages 1-13, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Chun-Tang Chao & Ding-Horng Chen & Juing-Shian Chiou, 2021. "Stability Analysis and Robust Stabilization of Uncertain Fuzzy Time-Delay Systems," Mathematics, MDPI, vol. 9(19), pages 1-13, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:24:p:4933-:d:1298587. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.