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

Research output: Contribution to journalArticle


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 languageEnglish
Article number106095
JournalApplied Soft Computing Journal
Publication statusPublished - Apr 2020



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

ASJC Scopus subject areas

  • Software

Cite this

Cortés-Antonio, P., Batyrshin, I., Martínez-Cruz, A., Villa-Vargas, L. A., Ramírez-Salinas, M. A., Rudas, I., Castillo, O., & Molina-Lozano, H. (2020). Learning rules for Sugeno ANFIS with parametric conjunction operations. Applied Soft Computing Journal, 89, [106095]. https://doi.org/10.1016/j.asoc.2020.106095