A self-organized neural network for blind separation process with unobservable sources
Chan-Cheng Liu, Tsung-Ying Sun, Chun‐Ling Lin, Chih-Ping Chou · 2005
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 /spl xi/.The separating number of sources estimated accurately by experiment of computer simulations.