A conventional auto-associative neural network separates blind sources without adding intentional algorithms other than pruning

A conventional auto-associative neural network (AANN) is shown to have an intrinsic ability to solve the blind source separation (BSS) problem without special computations explicitly intended for BSS, except for a pruning mechanism to deal with the usual case in which the number of the sources is unknown; each nonlinear hidden unit that has survived the pruning would recover one of the source signals. The feasibility of this non-information-theoretic approach is shown by computer simulation for twoand three-source examples involving various pdf´s for the independent sources. A mathematical analysis is made to discuss BSS in the context of local minima associated with the nonlinearity-induced error in the identity transformation by the AANN