BLIND SIGNAL SEPARATION USING CELLULAR NEURAL NETWORKS

In this paper a two-layer cellular neural network (CNN) is used to separate blind signals. The topological structures of the CNN and the inner parameters are presented. The first CNN layer functions as an adaptive filter which converges asymptotically to an equilibrium point in the mean. A stochastic stability model is used to find conditions under which cells in the first layer converge. Conditions leading to correct equilibrium solutions are also presented using this model. The second CNN layer functions as a signal separator. Simulations show that the CNN blind signal separator has strong robustness and works even better than the theory predicts.