Extended Particle Swarm Optimiser with Adaptive Acceleration Coefficients and Its Application in Nonlinear Blind Source Separation

2006 ◽  
pp. 1174-1182 ◽  
Author(s):  
Ying Gao ◽  
Zhaohui Li ◽  
Hui Zheng ◽  
Huailiang Liu
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Particle Swarm

Post-nonlinear blind source separation with kurtosis constraints using augmented Lagrangian particle swarm optimization and its application to mechanical systems

2019 ◽  
Vol 25 (16) ◽  
pp. 2246-2260 ◽  
Author(s):  
Jiantao Lu ◽  
Wei Cheng ◽  
Yapeng Chu ◽  
Yanyang Zi
Keyword(s):  
Particle Swarm Optimization ◽  
Case Studies ◽  
Blind Source Separation ◽  
Augmented Lagrangian ◽  
Source Separation ◽  
Particle Swarm ◽  
Lagrangian Multiplier ◽  
Lagrangian Particle ◽  
Swarm Optimization ◽  
Source Signals

To accurately estimate source signals from their post-nonlinear mixtures, a post-nonlinear blind source separation (PNLBSS) method with kurtosis constraints is proposed based on augmented Lagrangian particle swarm optimization (PSO). First, an improved contrast function is presented by combining mutual information of the separated signals and kurtosis ranges of source signals. Second, an augmented Lagrangian multiplier method is used to convert PNLBSS into an unconstrained pseudo-objective optimization problem. Then, improved PSO is applied to update the parameters in complex nonlinear spaces. Finally, numerical case studies and experimental case studies are provided to evaluate the performance of the proposed method. By adding the kurtosis ranges constraints, the estimation accuracy of source signals could be improved, which would benefit vibration and acoustic monitoring and control.

Research on Blind Source Separation Algorithm Based on Particle Swarm Optimization

2014 ◽  
Vol 989-994 ◽  
pp. 1566-1569 ◽  
Author(s):  
Hao Zhou ◽  
Chang Zheng Chen ◽  
Xian Ming Sun ◽  
Huan Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Blind Source Separation ◽  
Probability Distributions ◽  
Source Separation ◽  
Particle Swarm ◽  
Learning Rate ◽  
Gradient Algorithm ◽  
Swarm Optimization ◽  
Priori Information ◽  
Source Signals

Blind source separation (BSS) is a technique for recovering a set of source signals without priori information on the transformation matrix or the probability distributions of the source signals. In the previous works of BSS, the choice of the learning rate would reflect a trade-off between the stability and the speed of convergence. In this paper, a particle swarm optimization (PSO)-based learning rate adjustment method is proposed for BSS. In the simulations, three source signals are mixed and separated and the results are compared with natural gradient algorithm. The proposed approach exhibits rapid convergence, and produces more efficient and more stable independent component analysis algorithms than other related approaches.

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