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

Author

Moreau, Eric ; Macchi, Odile

Author_Institution

Lab. des Signaux et Syst., CNRS-Supelec-Univ., Gif sur Yvette, France

Volume

46

Issue

1

fYear

1998

fDate

1/1/1998 12:00:00 AM

Firstpage

39

Lastpage

50

Abstract

For pt.I see ibid., vol.45, p.918-26 (1997). Macchi and Moreau (1997) 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 Herault-Jutten algorithm. In this second part, we study the corresponding feedback adaptive network. For two globally sub-Gaussian sources, the network achieves quasi-convergence 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

Keywords

adaptive signal processing; computational complexity; feedback; neural nets; numerical stability; Herault-Jutten algorithm; complexity; convergence; direct linear adaptive neural network; direct linear network; feedback adaptive network; feedback linear network; globally sub-Gaussian sources; independent sources; mean square; mixed adaptive direct/feedback network; mixed linear network; quasi-convergence; self-adaptive source separation; separating state; stability; Adaptive control; Adaptive systems; Convergence; Feedback; Neural networks; Neurofeedback; Programmable control; Source separation; Stability; Steady-state;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.651167

Filename

651167