A New Fast Nonlinear Principal Component Analysis Algorithm for Blind Source Separation

被引：0
|
作者：
Wang, Xin [1 ]
Ou, Shifeng [1 ]
Gao, Ying [1 ]
Guo, Xiaofeng [1 ]
机构：
[1] Yantai Univ, Sch Optoelect Informat Sci & Technol, Yantai, Peoples R China
来源：
2015 12TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD) | 2015年
关键词：
blind source separation; nonlinear principal component; step size; momentum factor;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The nonlinear principal component analysis (NPCA) can be applied to solve the blind source separation (BSS) problem. By combining the optimum step size with the optimum momentum factor both derived by the decrement of the cost function of NPCA algorithm, an integration fast NPCA algorithm is proposed in this paper. Simulation experiments proved that the proposed algorithm is superior to other NPCA algorithms in convergence rate and steady error.
引用
收藏
页码：1626 / 1630
页数：5
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