Nonlinear blind separation using an RBF network model

Ying Tan, Jun Wang

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)

Abstract

A novel neural network approach is developed for nonlinear blind separation using a radial basis function (RBF) network and an information theoretic criterion. By utilizing the universal approximation apability and local response property of an RBF network the proposed separation method is characterized by fast convergence rate and strong demixing apability. After its learning process, the RBF network is able to separate independent signals effectively from their nonlinear mixtures by a the nonlinear channel model without the priori knowledge of the source signals and mixing channels. Experimental results illustrate the validity and effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)III-634-III-637
JournalProceedings - IEEE International Symposium on Circuits and Systems
Volume3
DOIs
Publication statusPublished - 2000
Externally publishedYes
EventProceedings of the IEEE 2000 International Symposium on Circuits and Systems, ISCAS 2000 - Geneva, Switz, Switzerland
Duration: 28 May 200031 May 2000

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