%0 Journal Article
%T Improved self-adaptive mixing neural network algorithm forblind source separation<br>一种改进的自适应混合神经网络盲分离算法
%A LV Shu-ping
%A ZHU Jie
%A <br>吕淑平
%A 祝　捷
%J 计算机应用研究
%D 2013
%I 
%X 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.
%K 盲信号分离
%K 神经网络
%K 自适应步长
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=DD8ECC43358E540DCBF9FF5C45B17D4B&yid=FF7AA908D58E97FA&vid=340AC2BF8E7AB4FD&iid=E158A972A605785F&sid=309A221A57FE8496&eid=C6E83222BCBFFFF8&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=9