Conference Proceeding

An improved natural gradient algorithm for blind source separation

702 Lab. Electron. Eng. Inst., Hefei, China
04/2010; DOI:10.1109/CAR.2010.5456777 pp.60 - 63 In proceeding of: Informatics in Control, Automation and Robotics (CAR), 2010 2nd International Asia Conference on, Volume: 1
Source: IEEE Xplore

ABSTRACT This paper proposes an improved natural gradient algorithm for blind source separation (BSS) based on the constrained optimization method. The improved algorithm introduces a scaling factor that restricts the training process by the balance spot, which adds little computational complexity and overcomes the conflict between the convergence rate and the steady-state accuracy. Therefore, the new algorithm exhibits fast convergence and excellent performance. Computer simulation results show that the new algorithm is effective. And compared with the conventional natural gradient algorithm and the adaptive step-size algorithm, the performance of the improved algorithm is obviously better.

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Keywords

adaptive step-size algorithm
 
balance spot
 
computational complexity
 
conventional natural gradient algorithm
 
excellent performance
 
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improved natural gradient algorithm
 
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new algorithm exhibits fast convergence
 
overcomes
 
scaling factor
 
steady-state accuracy
 
training process