| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 409572 | Neurocomputing | 2006 | 5 Pages |
Abstract
This paper addresses the problem of blind source separation (BSS) and presents an optimum step size which makes the nonlinear principal component analysis (NPCA) cost function descend in the fastest way. By applying this step size in the self-stabilized NPCA algorithm, a fast NPCA algorithm is obtained. Computer simulations of online BSS show that the new algorithm works more efficiently than the existing least-mean-square (LMS)-type and recursive least-squares (RLS)-type NPCA algorithms.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Xiaolong Zhu, Xianda Zhang, Yongtao Su,