GPSO-ICA: Independent Component Analysis based on Gravitational Particle Swarm Optimization for blind source separation

Shih Hsiung Lee, Chu-Sing Yang

Research output: Contribution to journalArticle

Abstract

Blind source separation (BSS) is an advanced method of signal processing. Essentially, the problem in BSS is to separate and estimate the original signal from the observed mixed signal source without knowing the characteristics of the original signal. Independent component analysis (ICA) is a popular approach for blind source separation, and because its traditional search scheme is based on a gradient algorithm, a convergence problem will arise. In order to overcome the defect, this paper proposed to apply Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) to conduct accelerated computing of the rate of convergence of a demixing matrix in ICA. However, the PSO converges prematurely, and the population diversity is reduced rapidly, so that the optimal solution falls into the local optimum. In order to increase the diversity of PSO, GPSO-based ICA algorithm (GPSO-ICA) is proposed that has the exploring ability of GSA, so that the ICA algorithm has a higher convergence rate and better ability to escape local optimization. A series of comparisons is implemented for the ICA algorithms based on PSO, GSA, and GPSO. The results show that GPSO-ICA has better performance than the other methods.

Original languageEnglish
Pages (from-to)1943-1957
Number of pages15
JournalJournal of Intelligent and Fuzzy Systems
Volume35
Issue number2
DOIs
Publication statusPublished - 2018 Jan 1

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Blind source separation
Blind Source Separation
Algorithm Analysis
Independent component analysis
Independent Component Analysis
Particle swarm optimization (PSO)
Particle Swarm Optimization
Search Algorithm
Population Diversity
Local Optimization
Gradient Algorithm
Signal Processing
Convergence Rate
Rate of Convergence
Defects
Optimal Solution
Converge
Series
Computing
Signal processing

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Engineering(all)
  • Artificial Intelligence

Cite this

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abstract = "Blind source separation (BSS) is an advanced method of signal processing. Essentially, the problem in BSS is to separate and estimate the original signal from the observed mixed signal source without knowing the characteristics of the original signal. Independent component analysis (ICA) is a popular approach for blind source separation, and because its traditional search scheme is based on a gradient algorithm, a convergence problem will arise. In order to overcome the defect, this paper proposed to apply Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) to conduct accelerated computing of the rate of convergence of a demixing matrix in ICA. However, the PSO converges prematurely, and the population diversity is reduced rapidly, so that the optimal solution falls into the local optimum. In order to increase the diversity of PSO, GPSO-based ICA algorithm (GPSO-ICA) is proposed that has the exploring ability of GSA, so that the ICA algorithm has a higher convergence rate and better ability to escape local optimization. A series of comparisons is implemented for the ICA algorithms based on PSO, GSA, and GPSO. The results show that GPSO-ICA has better performance than the other methods.",
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GPSO-ICA : Independent Component Analysis based on Gravitational Particle Swarm Optimization for blind source separation. / Lee, Shih Hsiung; Yang, Chu-Sing.

In: Journal of Intelligent and Fuzzy Systems, Vol. 35, No. 2, 01.01.2018, p. 1943-1957.

Research output: Contribution to journalArticle

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