Learning rules for Sugeno ANFIS with parametric conjunction operations

Prometeo Cortés-Antonio; Ildar Batyrshin; Alfonso Martínez-Cruz; Luis A. Villa-Vargas; Marco A. Ramírez-Salinas; Imre Rudas; Oscar Castillo; Herón Molina-Lozano

doi:10.1016/j.asoc.2020.106095

Learning rules for Sugeno ANFIS with parametric conjunction operations

Prometeo Cortés-Antonio, Ildar Batyrshin, Alfonso Martínez-Cruz, Luis A. Villa-Vargas, Marco A. Ramírez-Salinas, Imre Rudas, Oscar Castillo, Herón Molina-Lozano

Centro de Investigación en Computación (CIC)

Research output: Contribution to journal › Article › peer-review

14 Scopus citations

Abstract

The paper presents a Sugeno Adaptive Neuro-Fuzzy Inference System with parametric conjunction operations architecture, ANFIS-CX. The advantages of using parametric conjunction operations in fuzzy models are discussed, and learning rules for system identification with such operations are proposed. These learning strategies can include steepest descent gradient, differential evolution and least square estimation algorithms for tuning antecedent, conjunction, and consequent parameters, respectively. The results of system identification by parameter tuning of conjunction operations in addition to or instead of parameter tuning of the input membership functions are presented. Simulation results show that parameter training in conjunction operations, composed of four basic t-norms, significantly improves the approximation capability of fuzzy models.

Original language	English
Article number	106095
Journal	Applied Soft Computing Journal
Volume	89
DOIs	https://doi.org/10.1016/j.asoc.2020.106095
State	Published - Apr 2020

Keywords

ANFIS
Differential evolution algorithm
Fuzzy system
Learning rule
Parametric fuzzy conjunction
t-norm

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10.1016/j.asoc.2020.106095

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title = "Learning rules for Sugeno ANFIS with parametric conjunction operations",

abstract = "The paper presents a Sugeno Adaptive Neuro-Fuzzy Inference System with parametric conjunction operations architecture, ANFIS-CX. The advantages of using parametric conjunction operations in fuzzy models are discussed, and learning rules for system identification with such operations are proposed. These learning strategies can include steepest descent gradient, differential evolution and least square estimation algorithms for tuning antecedent, conjunction, and consequent parameters, respectively. The results of system identification by parameter tuning of conjunction operations in addition to or instead of parameter tuning of the input membership functions are presented. Simulation results show that parameter training in conjunction operations, composed of four basic t-norms, significantly improves the approximation capability of fuzzy models.",

keywords = "ANFIS, Differential evolution algorithm, Fuzzy system, Learning rule, Parametric fuzzy conjunction, t-norm",

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AU - Cortés-Antonio, Prometeo

AU - Batyrshin, Ildar

AU - Martínez-Cruz, Alfonso

AU - Villa-Vargas, Luis A.

AU - Ramírez-Salinas, Marco A.

AU - Rudas, Imre

AU - Castillo, Oscar

AU - Molina-Lozano, Herón

PY - 2020/4

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N2 - The paper presents a Sugeno Adaptive Neuro-Fuzzy Inference System with parametric conjunction operations architecture, ANFIS-CX. The advantages of using parametric conjunction operations in fuzzy models are discussed, and learning rules for system identification with such operations are proposed. These learning strategies can include steepest descent gradient, differential evolution and least square estimation algorithms for tuning antecedent, conjunction, and consequent parameters, respectively. The results of system identification by parameter tuning of conjunction operations in addition to or instead of parameter tuning of the input membership functions are presented. Simulation results show that parameter training in conjunction operations, composed of four basic t-norms, significantly improves the approximation capability of fuzzy models.

AB - The paper presents a Sugeno Adaptive Neuro-Fuzzy Inference System with parametric conjunction operations architecture, ANFIS-CX. The advantages of using parametric conjunction operations in fuzzy models are discussed, and learning rules for system identification with such operations are proposed. These learning strategies can include steepest descent gradient, differential evolution and least square estimation algorithms for tuning antecedent, conjunction, and consequent parameters, respectively. The results of system identification by parameter tuning of conjunction operations in addition to or instead of parameter tuning of the input membership functions are presented. Simulation results show that parameter training in conjunction operations, composed of four basic t-norms, significantly improves the approximation capability of fuzzy models.

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KW - Differential evolution algorithm

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Learning rules for Sugeno ANFIS with parametric conjunction operations

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Keywords

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