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This work proposed a blind source separation method by RBF neural network optimized by GA, which can improve the separation performance under low SNR condition. The center value and the width value of RBF can be determined by k-mean clustering algorithm and the cost function is set by maximum entropy. For RBF neural network is sensitive to noise, the blind source separation algorithm (BSS) is optimized by GA to obtain the optimal parameters of RBF neural network. This method can implement good separation results under low SNR condition and has better robustness compared with traditional RBF neural network. The computer simulation results show the effectiveness of the proposed method.

Blind Source Separation by RBF Neural Network Optimized by GA