A CLASS OF NEURAL NETWORKS FOR INDEPENDENT COMPONENT ANALYSIS

Independent component analysis (ICA) is a recently developed, useful e xtension of standard principal component analysis (PCA). The ICA model is utilized mainly in blind separation of unknown source signals from their linear mixtures, In this application only the source signals wh ich correspond to the coefficients of the ICA expansion are of interes t. In this paper, we propose neural structures related to multilayer f eedforward networks for performing complete ICA, The basic ICA network consists of whitening, separation, and basis vector estimation layers , It can be used for both blind source separation and estimation of th e basis vectors of ICA, We consider learning algorithms for each layer , and modify our previous nonlinear PCA type algorithms so that their separation capabilities are greatly improved, The proposed class of ne tworks yields good results in test examples with both artificial and r eal-world data.