A hybrid blind signal separation algorithm: particle swarm optimization on feed-forward neural network

  • Authors:

  • Chan-Cheng Liu;Tsung-Ying Sun;Sheng-Ta Hsieh;Chun-Ling Lin;Kan-Yuan Lee

  • Affiliations:

  • Intelligent Signal Processing Lab., Department of Electrical Engineering, National Dong Hwa University, Shoufeng, Hualien, Taiwan, R.O.C.;Intelligent Signal Processing Lab., Department of Electrical Engineering, National Dong Hwa University, Shoufeng, Hualien, Taiwan, R.O.C.;Intelligent Signal Processing Lab., Department of Electrical Engineering, National Dong Hwa University, Shoufeng, Hualien, Taiwan, R.O.C.;Intelligent Signal Processing Lab., Department of Electrical Engineering, National Dong Hwa University, Shoufeng, Hualien, Taiwan, R.O.C.;Intelligent Signal Processing Lab., Department of Electrical Engineering, National Dong Hwa University, Shoufeng, Hualien, Taiwan, R.O.C.

  • Venue:
  • ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

The blind signal separation problem (BSS) which involved linear mixing model and stationary source signals is focused in this paper. In the past, the neural network (NN) model is the popular architecture for separation, but its performance depends on initiation of weight strongly. In order to improve this problem to enhance global convergent, the genetic algorithm (GA) has been introduced for optimizing the weights of NN system recently. This paper, a novel evolution algorithm, particle swarm optimization (PSO) is introduced to optimize NN weights by us. Further, in simulation experiments of BSS, it is demonstrated that the PSO-based NN system has better performance in terms of global searching, computational time, accuracy and efficiency than the GA-based NN system.