DocumentCode :
1254883
Title :
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
Link To Document :
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