%0 Journal Article
%T A CLASS OF APPROACHES FOR BLIND SOURCE SEPARATION BASED ON MULTIVARIATE DENSITY ESTIMATION<br>一类基于多变量密度估计的盲源分离方法
%A He Zhenya
%A Yang Luxi
%A Liu Ju
%A Lu Ziyi
%A He Chen
%A <br>何振亚
%A 杨绿溪
%A 刘琚
%A 鲁子奕
%A 何晨
%J 电子与信息学报
%D 2001
%I 
%X A class of learning algorithms is drived for blind separation of independent source signals in this paper. These algorithms are based on minimizing a contrast function defined in terms of the Kullback-Leibler distance. By utilizing the technique of multivariate density esti-mation, two types of separating algorithms are obtained. Simulations illustrate the effectiveness of the algorithms.
%K Blind sources separation
%K Multivariate density estimation
%K Mutual information
%K Statistical independent<br>盲源分离
%K 多变量密度估计
%K 信号分析
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=8538E2C85C83CE42&yid=14E7EF987E4155E6&vid=EA389574707BDED3&iid=E158A972A605785F&sid=3622B70F9C54A9CC&eid=A5111BA190517959&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=0&reference_num=10