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论文类型： 期刊论文

发表时间： 2012-01-01

发表刊物： ICIC Express Letters

收录刊物： EI、Scopus

卷号： 6

期号： 9

页面范围： 2357-2362

ISSN号： 1881803X

摘要： A novel Independent Component Analysis algorithm based on parameterized model (PMICA) is proposed which can separate the mixture of super- and sub-Gaussian sources. Two approximate probability density function models are proposed respectively. In the framework of natural gradient, the parameters of two models are adaptively manipulated by online kurtosis learning. Stability analysis is discussed which provides the ranges of models parameters. Experiments show that the proposed PMICA algorithm has better performance than some other algorithms. ? 2012 ICIC International.