Self-adaptive source separation. II. Comparison of the direct, feedback, and mixed linear network

In the first part of the present paper, we investigated stability and convergence of a new direct linear adaptive neural network intended for separating independent sources when it is controlled by the well-known Hérault–Jutten algorithm. In this second part, we study the corresponding feedback adaptive network. For two globally sub-Gaussian sources, the network achieves quasiconvergence in the mean square sense toward a separating state. A novel mixed adaptive direct/feedback network that is free of implementation constraints is investigated from the points of view of stability and convergence and compared with the direct and feedback networks. The three networks have the same (low) complexity. The mixed one achieves the best trade-off between convergence speed and steady-state separation performance, independently of the specific mixture.