Blind separation of circularly distributed sources by neural extended APEX algorithm

The aim of this work is to present a generalized Hebbian learning theory for complex-weighted linear feed-forward network endowed with lateral inhibitory connections, and to show how it can be applied to blind separation from complex-valued mixtures. We start by stating an optimization principle for Kung-Diamantaras' network which leads to a generalized APEX-like learning theory relying on some non-linear functions, whose choice determines network's ability. Then we recall the Sudjianto-Hassoun interpretation of Hebbian learning and show that it drives us to the choice of the right set of non-linear functions allowing the network to achieve blind separation. The proposed approach is finally assessed by numerical simulations. (C) 2000 Published by Elsevier Science B.V. All rights reserved.