NATURAL GRADIENT ALGORITHM BASED ON A CLASS OF ACTIVATION FUNCTIONS AND ITS APPLICATIONS IN BSS
Blind source separation has become a dominant domain of artificial neural network. It attempts to recover unknown independent sources from a given set of observed mixtures. The natural gradient algorithm is a very important approach for blind source separation (BSS). The selection of activation function is the key to the algorithm. The aim of this paper is to investigate the blind source separation of a linear mixture of independent communication signals by using the natural gradient algorithm. We compare various activation functions for the algorithm and propose a better one. Simulation results not only demonstrate the algorithm can effectively separate the two kinds of random mixing signals, but also show that the algorithm with proposed activation function converges faster than other activation functions.
Blind source separation natural gradient algorithm activation function
LEI LI YU WANG XING-HUI WANG
Faculty of Mathematics and Physics, Nanjing University of Post and Telecommunications, Nanjing 210003, P.R.China
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
2985-2989
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）