A Novel Interactively Recurrent Self-Evolving Fuzzy CMAC and Its Classification Applications

Jyun Guo Wang, Shen Chuan Tai, Cheng Jian Lin

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


In this paper, an Interactively Recurrent Self-evolving Fuzzy Cerebellar Model Articulation Controller (IRSFCMAC) model is developed for solving classification problems. The proposed IRSFCMAC classifier consists of internal feedback and external loops, which are generated by the hypercube cell firing strength to itself and other hypercube cells. The learning process of the IRSFCMAC gets started with an empty hypercube base, and then all of hypercube cells are generated and learned online via structure and parameter learning, respectively. The structure learning algorithm is based on the degree measure to determine the number of hypercube cells. The parameter learning algorithm, based on the gradient descent method, adjusts the shapes of the membership functions and the corresponding fuzzy weights of the IRSFCMAC. Finally, the proposed IRSFCMAC model is tested by four benchmark classification problems. Experimental results show that the proposed IRSFCMAC model has superior performance than traditional FCMAC and other models.

Original languageEnglish
Article number1550019
JournalInternational Journal of Computational Intelligence and Applications
Issue number3
Publication statusPublished - 2015 Sep 19

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Computer Science Applications


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