Fuzzy neural-based learning rate adjustment for gradient based blind source separation

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

*此作品的通信作者

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

Independent component analysis (ICA) algorithms have been proposed to solve blind source separation (BSS) problem in recent years. T he gradient algorithm is a popular method deals with separating independent signal step by step with learning rate. In this paper, consider to balance the mis-adjustment and the speed of convergence, the leaning rate will be computed in fuzzy neural network (FNN) depended on the second-order and higher order correlation coefficients of output components of BSS. To enhance the performance of the FNN-based learning rate, the FNN is optimization by particle swarm optimization algorithm. Finally, simulation results are shown to illustrate the effectiveness of the proposed method.

原文English
主出版物標題Proceedings - International Conference on Machine Learning and Cybernetics
發行者IEEE Computer Society
頁面1450-1455
頁數6
ISBN(電子)9781479902576
DOIs
出版狀態Published - 2013
事件12th International Conference on Machine Learning and Cybernetics, ICMLC 2013 - Tianjin, China
持續時間: 14 7月 201317 7月 2013

出版系列

名字Proceedings - International Conference on Machine Learning and Cybernetics
3
ISSN(列印)2160-133X
ISSN(電子)2160-1348

Conference

Conference12th International Conference on Machine Learning and Cybernetics, ICMLC 2013
國家/地區China
城市Tianjin
期間14/07/1317/07/13

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