ROBUST LEARNING ALGORITHM FOR BLIND SEPARATION OF SIGNALS

被引：118

作者：

CICHOCKI, A

UNBEHAUEN, R

RUMMERT, E

机构：

[1] Allgemeine und Theoretische Elektrotechnik, University Erlangen-Nürnberg, D-91058 Erlangen

来源：

ELECTRONICS LETTERS | 1994年 / 30卷 / 17期

关键词：

NEURAL NETS; LEARNING SYSTEMS; SIGNAL PROCESSING;

D O I：

10.1049/el:19940956

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The authors present a novel, efficient, self-normalising, unsupervised adaptive learning algorithm for the on-line (realtime) separation of statistically independent unknown source signals from a linear mixture of them. In contrast to the known algorithms the new algorithm allows the separation (or extraction) of extremely badly scaled signals (i.e. some or even all of the source and/or sensor signals can be very weak). Moreover, the mixing matrix can be very iii-conditioned.

引用

页码：1386 / 1387

页数：2

共 10 条

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