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- W1989953089 abstract "This paper addresses the problem of blind source separation (BSS) of independent sources from their linear mixtures in the over-determined cases ( ) with unknown and dynamically changing number of sources. The system architecture including an on-line source number estimator and an auto-adjust separation mechanism is considered based on the feed-forward neural network (FNN). To speed up and stabilize the iteration procedure, we propose to modify the FNN by adding a momentum term, and convergence analysis for the new algorithm is also presented, provided that the learning rate is set as a constant and the momentum factor an adaptive variable. Computer simulation results confirm that our approach is feasible for dynamic BSS cases and has satisfied convergence speed and steady-state error performance. Moreover, the proposed algorithm can ensure the separation of weak or badly scaled signals." @default.
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- W1989953089 date "2011-05-04" @default.
- W1989953089 modified "2023-09-26" @default.
- W1989953089 title "Neural Network with Momentum for Dynamic Source Separation and its Convergence Analysis" @default.
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- W1989953089 doi "https://doi.org/10.4304/jnw.6.5.791-798" @default.
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