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论文类型：期刊论文

发表时间：2008-10-01

发表刊物：NEUROCOMPUTING

收录刊物：SCIE、EI、Scopus

卷号：71

期号：16-18,SI

页面范围：3656-3659

ISSN号：0925-2312

关键字：Independent component analysis; Blind source separation; Gaussian moments; Nonstationary variance; Autocorrelations

摘要：A unifying model that combines three properties is proposed by Hyvarinen, and a gradient ascent algorithm for independent component analysis (ICA) is performed by maximum likelihood estimation. In this paper, we consider the estimation of the data model of ICA when Gaussian noise is present and the independent components are time dependent. Firstly, according to the useful property of Gaussian moments, we introduce Gaussian moments algorithm to estimation of the noisy unifying model when the noise covariance matrix is known. Next, when the noise covariance is unknown, a new Gaussian moments algorithm is developed. Finally, the validity and performance of our algorithms are demonstrated by computer simulations. Crown Copyright (C) 2008 Published by Elsevier B.V. All rights reserved.