A Cascade Neural Network for Blind Signal Extraction without Spurious Equilibria

We present a cascade neural network for blind source extraction We propose a family of unconstrained opti mization criteria from which we derive a learning rule that can extract a single source signal from a linear mixture of source sig nals To prevent the newly extracted source signal from being extracted again in the next processing unit we propose another unconstrained optimization criterion that uses knowledge of this signal From this criterion we then derive a learning rule that de ates from the mixture the newly extracted signal By virtue of blind extraction and de ation processing the presented cascade neural network can cope with a practical case where the number of mixed signals is equal to or larger than the number of sources with the number of sources not known in advance We prove an alytically that the proposed criteria both for blind extraction and de ation processing have no spurious equilibria In addition the proposed criteria do not require whitening of mixed signals We also demonstrate the validity and performance of the presented neural network by computer simulation experiments key words blind source separation and extraction neural net works on line adaptive algorithms