Title :
General multilayer perceptron demixer scheme for nonlinear blind signal separation
Author :
Woo, W.L. ; Sali, S.
Author_Institution :
Dept. of Electr. & Electron. Eng., Newcastle upon Tyne Univ., UK
fDate :
10/1/2002 12:00:00 AM
Abstract :
A new technique is presented for instantaneous blind signal separation from nonlinear mixtures using a general neural network based demixer scheme. The nonlinear demixer model follows directly from the general mixer model. A general mixer model is described which includes linear mixtures as a special case. In the second part the general framework for a demixer based on a feedforward multilayer perceptron (FMLP) employing a class of continuously differentiable nonlinear functions is presented. A detailed derivation of the learning algorithm used to adapt the demixer´s parameters is given. Cost functions based on both maximum entropy (ME) and minimum mutual information (MMI) have been studied. The performance of the new technique was investigated using various experiments derived from the general mixer model and using real-time data. These studies illustrated the superiority and the generality of the new technique compared with existing methods.
Keywords :
blind source separation; feedforward neural nets; learning (artificial intelligence); multilayer perceptrons; nonlinear functions; continuously differentiable nonlinear functions; cost functions; demixer parameters; feedforward multilayer perceptron; general mixer model; general multilayer perceptron demixer; general neural network based demixer; learning algorithm; maximum entropy; minimum mutual information; nonlinear blind signal separation; nonlinear demixer model; nonlinear mixtures; real-time data;
Journal_Title :
Vision, Image and Signal Processing, IEE Proceedings -
DOI :
10.1049/ip-vis:20020548