电讯技术Issue(11)：1200-1205,6.DOI:10.3969/j.issn.1001-893x.2015.11.003
基于加权最小二乘支持向量机的欠定盲源分离
Undetermined Blind Source Separation Based on Weighted Least Square Support Vector Machine
摘要
Abstract
To further improve the performance of the underdetermined blind source separation algorithm,an algorithm based on weighted least square support vector machine( WLS-SVM) is proposed. Firstly,it esti-mates the number of source signal using the characteristic of frequency domain signal. Secondly, it uses WLS-SVM to obtain the initial weight values. The sample point corresponding to one of the weight values is used as the test sample every time,and the other is used as training sample. The error variable is upda-ted sequentially,and then all weight values are updated according to the weight calculation formula to de-termine the optimal classification plane and realize optimal classification of observed signals to estimate the mixed matrix. Simulation results prove that the proposed algorithm has smaller error compared with tradi-tional algorithm. The error of proposed algorithm is twenty percent of that of K-means based method,and fifty percent of that of SVM-based method.关键词
欠定盲源分离/加权最小二乘支持向量机/K-均值聚类/矩阵估计Key words
undetermined blind source separation/weighted least square support vector machine/K-means clustering/matrix estimation分类
信息技术与安全科学引用本文复制引用
赵立权,刘珊珊..基于加权最小二乘支持向量机的欠定盲源分离[J].电讯技术,2015,(11):1200-1205,6.基金项目
国家自然科学基金资助项目(61271115) （61271115）
吉林市科技发展项目(2013625009)Foundation Item:The National Natural Science Foundation of China(No.61271115) （2013625009）
The Scientific Research Fundation of the Education De-partment of Jilin Municipality(No.2013635009) （No.2013635009）
