Non-Holonomic constraints in learning blind source separation

被引：0

作者：

Amari, S ^{[1
]}

Chen, TP ^{[1
]}

Cichocki, A ^{[1
]}

机构：

[1] RIKEN, Frontier Res Program, Wako, Saitama 35101, Japan

来源：

PROGRESS IN CONNECTIONIST-BASED INFORMATION SYSTEMS, VOLS 1 AND 2 | 1998年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Recently a number of adaptive learning algorithms have been proposed for blind source separation. Although the underlying principles and approaches are different, most of them have very similar forms. The algorithms are based on gradient decsent on some manifolds. In this paper, Non-Holonomic constraints are introduced and orthogonal natural gradient decsent algorithm is proposed. It is proved theoretically as well as by simulations that this algorithm has some advantage over other Holonomic constraints.

引用

页码：633 / 636

页数：4

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