基于分离度梯度的步长自适应自然梯度算法

首先定义了描述信号分离状态的分离度,并利用分离度作为参数来控制自然梯度算法中的步长因子,从而首次提出了一种基于分离状态的步长自适应自然梯度盲源分离算法。由于该算法步长是基于分离度的,其学习速率由信号的分离程度自适应地选取,因而能很好地解决收敛速度和稳态误差之间的矛盾。计算机仿真结果与理论分析相一致,证实了该算法明显优于其他固定步长或变步长的自然梯度算法。

Adaptive step-size natural gradient algorithm based on separating degree gradient

This paper proposes to use separating degree to control the step-size of natural algorithm for the first time. After detailed analyzing relevant fixed step-size and variable step-size gradient algorithms, the paper presents a new adaptive step-size natural algorithm. Because the variability of the new algorithm's step-size is based on separating degree, its learning ratio is chosen adaptively according to separating degree, therefore it can improve convergence speed and reduce the misadjustment error in the steady state simultaneously. Computer simulations confirm the theoretical analysis and show the algorithm performance is superior to other natural algorithms.