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A Hybrid Blind Signal Separation Algorithm: Particle Swarm Optimization on Feed-Forward Neural Network

  • Chan-Cheng Liu
  • Tsung-Ying Sun
  • Sheng-Ta Hsieh
  • Chun-Ling Lin
  • Kan-Yuan Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)

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.

Keywords

Genetic Algorithm Particle Swarm Optimization Independent Component Analysis Radial Basis Function Neural Network Independent Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chan-Cheng Liu
    • 1
  • Tsung-Ying Sun
    • 1
  • Sheng-Ta Hsieh
    • 1
  • Chun-Ling Lin
    • 1
  • Kan-Yuan Lee
    • 1
  1. 1.Intelligent Signal Processing Lab., Department of Electrical EngineeringNational Dong Hwa UniversityShoufeng, HualienTaiwan, R.O.C.

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