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

• Published in: IEEE transactions on evolutionary computation Vol. 12; no. 2; pp. 242 - 251
• Main Authors: HSIEH, Sheng-Ta, SUN, Tsung-Ying, LIN, Chun-Ling, LIU, Chan-Cheng
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.04.2008; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• 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); Blind source separation (BSS); particle swarm optimization (PSO); turnaround factor (TF); learning rate; Prior information; Source separation; Decision making; Evolutionary algorithm; Independent component analysis; Probability distribution; Neural network; Particle swarm optimization; Blind separation; Convergence speed; Signal sources; Swarm intelligence
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2007.898781

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.