Growing Particle Swarm Optimizers with a Population-Dependent Parameter
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Application of particle swarm optimizers to two-objective problems in design of switching inverters
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
An improved multi-objective particle swarm optimizer for multi-objective problems
Expert Systems with Applications: An International Journal
Heuristic wavelet shrinkage for denoising
Applied Soft Computing
A rank based particle swarm optimization algorithm with dynamic adaptation
Journal of Computational and Applied Mathematics
Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions
Information Sciences: an International Journal
Engineering Applications of Artificial Intelligence
The block diagram method for designing the particle swarm optimization algorithm
Journal of Global Optimization
Expert Systems with Applications: An International Journal
A novel self-constructing Radial Basis Function Neural-Fuzzy System
Applied Soft Computing
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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.