Title :
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
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;
Conference_Titel :
Intelligent Signal Processing and Communication Systems, 2005. ISPACS 2005. Proceedings of 2005 International Symposium on
Print_ISBN :
0-7803-9266-3
DOI :
10.1109/ISPACS.2005.1595375
