NEW LEARNING ALGORITHM FOR BLIND SEPARATION OF SOURCES

A new improved, easily implementible learning algorithm for blind separation of statistically independent unknown source signals is proposed. In contrast to the well known algorithms, two time trajectories of synaptic weights {w(ij)(t)} and {w(ij)(t)} are computed where w(ij)(t) is the time average of w(ij)(t). Extensive computer simulation experiments have confirmed that the proposed learning algorithm assures a high convergence speed of the neural network for a blind identification problem, i.e. a quick recovering of unknown signals from the observation of a linear combination (mixture) of them. The algorithm can easily be extended to other applications.