Adaptive Filter Design Using Type-2 Fuzzy Cerebellar Model Articulation Controller

Chih-Min Lin, Ming-Shu Yang, Fei Chao, Xiao-Min Hu, Jun Zhang

Research output: Contribution to journalArticlepeer-review

27 Citations (SciVal)


This paper aims to propose an efficient network and applies it as an adaptive filter for the signal processing problems. An adaptive filter is proposed using a novel interval type-2 fuzzy cerebellar model articulation controller (T2FCMAC). The T2FCMAC realizes an interval type-2 fuzzy logic system based on the structure of the CMAC. Due to the better ability of handling uncertainties, type-2 fuzzy sets can solve some complicated problems with outstanding effectiveness than type-1 fuzzy sets. In addition, the Lyapunov function is utilized to derive the conditions of the adaptive learning rates, so that the convergence of the filtering error can be guaranteed. In order to demonstrate the performance of the proposed adaptive T2FCMAC filter, it is tested in signal processing applications, including a nonlinear channel equalization system, a time-varying channel equalization system, and an adaptive noise cancellation system. The advantages of the proposed filter over the other adaptive filters are verified through simulations
Original languageEnglish
Pages (from-to)2084-2094
Number of pages11
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number10
Early online date29 Oct 2015
Publication statusPublished - 31 Oct 2016
Externally publishedYes


  • adaptive systems
  • convergence
  • adaptation models
  • neural networks
  • fuzzy sets
  • noise cancellation


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