A self-organized neural network for blind separation process with unobservable sources

Author

Liu, Chan-Cheng ; Sun, Tsung-Ying ; Lin, Chun-Ling ; Chou, Chih-Ping

Author_Institution

Dept. of Electical Eng., Nat. Dong Hwa Univ., Taiwan

fYear

2005

fDate

13-16 Dec. 2005

Firstpage

177

Lastpage

180

Abstract

A self-organized rule is proposed to process the separation of unknown number of sources of blind signal. The rule is applied to the feed-forward neural network (FFNN) which is based on a mean weighting gradient (MWG) function. The algorithms adjust the architecture of neural network. For both, the performances are the mixture of node number and MWG threshold ξ.The separating number of sources estimated accurately by experiment of computer simulations.

Keywords

blind source separation; feedforward neural nets; gradient methods; self-organising feature maps; blind separation process; blind signal sources; feed-forward neural network; mean weighting gradient function; self-organized neural network; unobservable sources; Blind source separation; Computer architecture; Computer simulation; Costs; Covariance matrix; Feedforward neural networks; Feedforward systems; Neural networks; Signal processing; Sun;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Signal Processing and Communication Systems, 2005. ISPACS 2005. Proceedings of 2005 International Symposium on

Print_ISBN

0-7803-9266-3

Type

conf

DOI

10.1109/ISPACS.2005.1595375

Filename

1595375

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3272983