TY - JOUR
T1 - Learning rules for Sugeno ANFIS with parametric conjunction operations
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
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/4
Y1 - 2020/4
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.
KW - ANFIS
KW - Differential evolution algorithm
KW - Fuzzy system
KW - Learning rule
KW - Parametric fuzzy conjunction
KW - t-norm
UR - http://www.scopus.com/inward/record.url?scp=85078763298&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2020.106095
DO - 10.1016/j.asoc.2020.106095
M3 - Artículo
AN - SCOPUS:85078763298
SN - 1568-4946
VL - 89
JO - Applied Soft Computing Journal
JF - Applied Soft Computing Journal
M1 - 106095
ER -