Blind Separation of Positive Signals by Using Genetic Algorithm

Authors:
Mao Ye;Zengan Gao;Xue Li
Affiliations:
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, P.R. China and Institute of Knowledge Management and Business Intelli ...;Institute of Knowledge Management and Business Intelligence, School of Economics and Management, Southwest Jiaotong University, Chengdu, Sichuan 610031, P.R. China;School of Information Technology and Electronic Engineering, The University of Queensland, Brisbane, Queensland 4072, Australia
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Year:
2007

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Abstract

When the source signals are known to be independent, positive and well-grounded which means that they have a non-zero pdf in the region of zero, a few algorithms have been proposed to separate these positive sources. However, in many practical cases, the independent assumption is not always satisfied. In this paper, a new approach is proposed to separate a class of positive sources which are not required to be independent. These source signals can be separated very quickly by using genetic algorithm. The objective function of genetic algorithm is derived from uncorrelated and some special assumptions on such positive source signals. Simulations are employed to illustrate the good performance of our algorithm.