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

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - International Conference on Machine Learning and Cybernetics
PublisherIEEE Computer Society
Pages1450-1455
Number of pages6
ISBN (Electronic)9781479902576
DOIs
StatePublished - 2013
Event12th International Conference on Machine Learning and Cybernetics, ICMLC 2013 - Tianjin, China
Duration: 14 Jul 201317 Jul 2013

Publication series

NameProceedings - International Conference on Machine Learning and Cybernetics
Volume3
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Conference

Conference12th International Conference on Machine Learning and Cybernetics, ICMLC 2013
CountryChina
CityTianjin
Period14/07/1317/07/13

Keywords

  • Blind source separation
  • Fuzzy neural network
  • Independent component analysis
  • Particle swarm optimization

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  • Cite this

    Lee, C. H., Huang, M. T., & Lin, C. M. (2013). Fuzzy neural-based learning rate adjustment for gradient based blind source separation. In Proceedings - International Conference on Machine Learning and Cybernetics (pp. 1450-1455). [6890810] (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 3). IEEE Computer Society. https://doi.org/10.1109/ICMLC.2013.6890810