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

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

*此作品的通信作者

研究成果: Article同行評審

3 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)411-421
頁數11
期刊International Journal of Fuzzy Systems
16
發行號3
出版狀態Published - 1 9月 2014

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