摘要
自然梯度算法有较快的收敛速度、良好的分离性能,在盲信号分离中占有重要地位。但该算法是基于固定步长的,所以不能很好地解决收敛速度与稳态误差之间的矛盾。通过建立步长因子与峭度的平方和之间的非线性关系,提出了一种自适应的自然梯度算法。计算机仿真结果证实了该算法的有效性,并说明了该算法明显优于自然梯度算法。
Because of quick convergence rate and good separation performance,natural gradient algorithm occupies importance position in blind source separation.Natural gradient algorithm adopts fix-step,so they cannot resolve the contradiction between convergence speed and the error in the steady state.By building a nonlinear function relationship between the step size factor and the square sum of the kurtosis,the paper proposes an adaptive natural gradient algorithm.Computer simulation result confirms the algorithm’s validity,and shows that the algorithm’s performance is superior to natural gradient algorithm.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第11期132-134,214,共4页
Computer Engineering and Applications
关键词
盲信号分离
自适应
学习率
峭度
blind source separation
adaptive
learning rate
kurtosis
