期刊
NEURAL NETWORKS
卷 20, 期 7, 页码 842-850出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2007.07.001
关键词
minor component analysis (MCA); deterministic discrete time (DDT); systems; eigenvector; eigenvalue
Minor component analysis (MCA) is a powerful statistical tool for signal processing and data analysis. Convergence of MCA learning algorithms is an important issue in practical applications. In this paper. we will propose a simple MCA learning algorithm to extract minor component from input signals. Dynamics of the proposed MCA learning algorithm are analysed using a corresponding deterministic discrete time (DDT) system. It is proved that almost all trajectories of the DDT system will converge to minor component if the learning rate satisfies some mild conditions and the trajectories start from points in an invariant set. Simulation results will be furnished to illustrate the theoretical results achieved. (C) 2007 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据