1. A Class of Sequential Blind Source Separation Method in Order Using Swarm Optimization Algorithm
- Author
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Zhan Yiju, Wang Rongjie, and Zhou Haifeng
- Subjects
0209 industrial biotechnology ,business.industry ,Applied Mathematics ,Particle swarm optimization ,Pattern recognition ,02 engineering and technology ,Maximization ,Independent component analysis ,Blind signal separation ,Artificial bee colony algorithm ,symbols.namesake ,020901 industrial engineering & automation ,Gaussian noise ,Differential evolution ,Signal Processing ,0202 electrical engineering, electronic engineering, information engineering ,Kurtosis ,symbols ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,Mathematics - Abstract
We consider the problem of sequential, blind source separation in some specific order from a mixture of sub- and sup-Gaussian sources. Three methods of separation are developed, specifically, kurtosis maximization using (a) particle swarm optimization, (b) differential evolution, and (c) artificial bee colony algorithm, all of which produce the separation in decreasing order of the absolute kurtosis based on the maximization of the kurtosis cost function. The validity of the methods was confirmed through simulation. Moreover, compared with other conventional methods, the proposed method separated the various sources with greater accuracy. Finally, we performed a real-world experiment to separate electroencephalogram (EEG) signals from a super-determined mixture with Gaussian noise. Whereas the conventional methods separate simultaneously EEG signals of interest along with noise, the result of this example shows the proposed methods recover from the outset solely those EEG signals of interest. This feature will be of benefit in many practical applications.
- Published
- 2015