基于K-均值聚类和势函数法的欠定盲分离

doi:10.3969/j.issn.1000-0801.2012.01.019

Abstract

Abstract:

K-means clustering method can estimate the observed clustering signal line direction,using principal component analysis can improve the accuracy and robustness of linear estimation.Under the guidance of this thinking,method of combine K-means clustering with potential function is proposed.Potential function measure the distance of clustering center with all observation points,based on the potential function find clustering center iteration formulas,using this formula to adjust clustering center.The presented algorithm is characterized by high accuracy and less computation.Simulation results illustrate the efficiency and the good performance of the algorithm.

Key words: blind source separation, sparse component analysis, potential function, clustering

Jing Yang,Yujie Zhang,Hongwei Li. Underdetermined Blind Source Separation Algorithm Based on K-Means Clustering and Potential Function[J]. Telecommunications Science, 2012, 28(1): 102-105.

Figures/Tables 3

	$ae (a_{1}, {\hat{a}}_{1})$	$ae (a_{2}, {\hat{a}}_{2})$	$ae (a_{3}, {\hat{a}}_{3})$	$ae (a_{3}, {\hat{a}}_{3})$
初估计	5.1839	0.6811	5.1084	2.4661
精估计	0.2253	0.1817	0.4264	0.2601

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Underdetermined Blind Source Separation Algorithm Based on K-Means Clustering and Potential Function

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