Title :
Nonlinear blind separation using an RBF network model
Author :
Tan, Ying ; Wang, Jun
Author_Institution :
Dept. of Mech. & Autom. Eng., Chinese Univ. of Hong Kong, Shatin, China
Abstract :
A novel neural network approach is developed for nonlinear blind separation using a radial b axis function (RBF) network and an information theoretic criterion. By utilizing the universal approximation ability and local response property of an RBF network the proposed separation method is characterized by fast convergence and strong demixing ability. After its learning process, the RBF network is able to separate independent signals effectively from their nonlinear mixtures by the nonlinear channel model without the prior knowledge of the source signals and mixing channels. Experimental results illustrate the validity and effectiveness of the proposed method
Keywords :
learning (artificial intelligence); radial basis function networks; signal detection; RBF network model; convergence; demixing ability; independent signals; information theoretic criterion; learning process; local response property; nonlinear blind separation; nonlinear channel model; radial b axis function; source signals; universal approximation ability; Automation; Biomedical signal processing; Blind source separation; Deconvolution; Neural networks; Radial basis function networks; Signal processing; Signal processing algorithms; Speech processing; Unsupervised learning;
Conference_Titel :
Circuits and Systems, 2000. Proceedings. ISCAS 2000 Geneva. The 2000 IEEE International Symposium on
Conference_Location :
Geneva
Print_ISBN :
0-7803-5482-6
DOI :
10.1109/ISCAS.2000.856140