Effective Learning Rate Adjustment of Blind Source Separation Based on an Improved Particle Swarm Optimizer

Blind source separation (BSS) is a technique used to recover a set of source signals without prior information on the transformation matrix or the probability distributions of the source signals. In previous works on BSS, the choice of the learning rate would result in a competition between stabilit...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation 2008-04, Vol.12 (2), p.242-251
Main Authors: HSIEH, Sheng-Ta, SUN, Tsung-Ying, LIN, Chun-Ling, LIU, Chan-Cheng
Format: Article
Language:English
Subjects:
Adaptive signal processing
Algorithms
Applied sciences
Artificial intelligence
Blind source separation
Blind source separation (BSS)
Blinds
Computer science
control theory
systems
Convergence
Exact sciences and technology
Independent component analysis
Learning
Learning and adaptive systems
learning rate
Least squares approximation
Optimization
Particle swarm optimization
particle swarm optimization (PSO)
Separation
Signal processing algorithms
Source separation
Stability
Sun
Transformations
turnaround factor (TF)
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Summary:Blind source separation (BSS) is a technique used to recover a set of source signals without prior information on the transformation matrix or the probability distributions of the source signals. In previous works on BSS, the choice of the learning rate would result in a competition between stability and speed of convergence. In this paper, a particle swarm optimization (PSO)-based learning rate adjustment method is proposed for BSS, and a simple decision-making method is introduced for how the learning rate should be applied in the current time slot. In the experiments, samples of four and ten source signals were mixed and separated and the results were compared with other related approaches. The proposed approach exhibits rapid convergence, and produces more efficient and more stable independent component analysis algorithms, than other related approaches.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2007.898781