Authors : D. Malathi and N. Gunasekaran
Abstract: This study proposes, a new learning algorithm for extracting the independent source signals from an artificially mixed signal. An adaptive self-normalized radial basis function neural network is developed and trained by the proposed learning algorithm to model the nonlinearity from the latent variables to the observations. The joint probability density function and marginal probability density functions are used to determine the inverse of the nonlinear mixing matrix, which is assumed to exist and able to be approximated. The centers of the ASN-RBF network are initialized with the weights between input and hidden layer to update the parameters in the generative model. This proposed algorithm is well-suited for nonlinear data analysis problems and theoretically interesting. Minimum 3 signals are considered for simulation. Simulation results show the feasibility of the proposed algorithm. The performance of the proposed network is compared with the Independent Component Analysis (ICA) algorithm and it is illustrated with computer simulated experiments.
D. Malathi and N. Gunasekaran , 2009. A Novel Neural Learning Algorithm for Separation of Blind Signals. International Journal of Soft Computing, 4: 16-24.