Type-2 fuzzy cerebellar model articulation controller-based learning rate adjustment for blind source separation

Meng Tzu Huang, Ching Hung Lee*, Chin Min Lin

*Corresponding author for this work

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Blind source separation (BSS) is a technique for recovering a set of source signals without a priori information on the transformation matrix or the probability distributions of source signals. Based on separation results of outputs, this paper proposes the interval type-2 fuzzy cerebellar model articulation controller (T2FCMAC)-based learning rate adjustment for the BSS. The adopted T2FCMAC system has the ability of generating the proper learning rate by using the inputs of second- and higher order correlation coefficients of output components. In addition, to enhance the performance of the T2FCMAC-based learning rate approach, the T2FCMAC system is optimized by particle swarm optimization (PSO) algorithm by the performance index of second-order correlation measure. Simulation and comparison results are introduced to show the effectiveness and performance of the proposed approach.

Original languageEnglish
Pages (from-to)411-421
Number of pages11
JournalInternational Journal of Fuzzy Systems
Volume16
Issue number3
StatePublished - 1 Sep 2014

Keywords

  • Blind source separation
  • Cerebellar model articulation controller
  • Independent component analysis
  • Interval type-2 fuzzy system
  • Particle swarm optimization

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