摘要
独立分量分析方法(ICA)是信号处理的一种新技术。其基本目标是寻找线性变换矩阵,将观测的多维混合信号进行变换,变换后的输出信号各分量之间尽可能统计独立。将遗传算法与ICA相结合,提出基于GA的盲分离算法,并分析了它们的收敛性和稳态性能。其有效性为仿真结果所证实。
Independent component analysis(ICA) is a new technique in signal processing .The goal of ICA is to find a separation matrix so that each component of the output signal obtained by transforming the observed multidimensional mixture of data is independent. A new genetic algorithm which combines GA with ICA for blind source separation is proposed in this paper. The convergence and stability of the algorithm are analysed and their validity is confirmed by the signal separation test.
出处
《武汉科技大学学报》
CAS
2003年第3期297-300,共4页
Journal of Wuhan University of Science and Technology
关键词
盲源分离
独立分量分析
遗传算法
blind source separation
independent component analysis
genetic algorithm
