Dual cascade networks for blind signal extraction

Andrzej Cichocki, Ruck Thawonmas, Shun Ichi Amari

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

A new neural-network approach is presented for extracting independent source signals one-by-one from a linear mixture of them when the number of noisy mixed signals is equal to or larger than the number of sources. In this approach, two types of cascade neural networks, having similar structures, are employed. The first cascade network performs prewhitening (preprocessing) of the mixed signals by sequentially extracting principal components. From the normalized (to unit variance) prewhitened signals, the second network, then sequentially extracts the original source signals in order according to their stochastic properties, namely, in decreasing order of absolute valves of normalized kurtosis. Extensive computer simulations confirm the validity and high performance of our approach.

Original languageEnglish
Title of host publication1997 IEEE International Conference on Neural Networks, ICNN 1997
Pages2135-2140
Number of pages6
DOIs
Publication statusPublished - 1997
Externally publishedYes
Event1997 IEEE International Conference on Neural Networks, ICNN 1997 - Houston, TX, United States
Duration: 9 Jun 199712 Jun 1997

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
Volume4
ISSN (Print)1098-7576

Conference

Conference1997 IEEE International Conference on Neural Networks, ICNN 1997
Country/TerritoryUnited States
CityHouston, TX
Period9/06/9712/06/97

Fingerprint

Dive into the research topics of 'Dual cascade networks for blind signal extraction'. Together they form a unique fingerprint.

Cite this