Space or time adaptive signal processing by neural network models
AIP Conference Proceedings 151 on Neural Networks for Computing
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Blind source separation using order statistics
Signal Processing
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
Sequential blind extraction of instantaneously mixed sources
IEEE Transactions on Signal Processing
A Markov model for blind image separation by a mean-field EM algorithm
IEEE Transactions on Image Processing
IEEE Transactions on Neural Networks
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In this paper, a novel identification of mixing matrix using genetic algorithm (GA) is proposed to deal with the blind sparse source separation (BSS) problem. A preprocessing filters the most of minor mixtures at first, and then represents the remainder in angle. Further, we regard a probable set of angle of mixing vectors as a chromosome of GA, and iterate the evolutionary loop to minimize the fitness function which summarizes the angle difference between mixtures and estimated mixing vector. In computer simulations, mixing matrixes with well-condition and ill-condition are considered for testing, meantime several algorithms are carried them out also. It was demonstrated by simulation results that the proposed GA-based algorithm is superior in validation and effectualness than others.