Improved self-adaptive mixing neural network algorithm forblind source separation
一种改进的自适应混合神经网络盲分离算法

The traditional feedforward neural network blind source separation algorithm is imperfect because of its fixed learning step. Although the self-adaptive step size algorithm based on Sigmoid-function can overcome the shortcomings of fixed step, its steady-state performance is poor. According to this problem, this paper proposed an improved self-adaptive step algorithm, which could flexibly control the shape of the step curve and the shape changed more slowly near the zeros than Sigmoid-function, the performance was more excellent. Secondly, considering the insufficient of feedforward neural network structure, this paper added a recursive structure into the whole model, adjusting learning step size with the improved self-adaptive step algorithm control algorithm. The simulation analysis shows that the algorithm has a faster separation speed and a better performance in convergence.