摘要
为解决固定步长自然梯度算法带来的收敛速率与稳态误差之间矛盾的问题,采用一种自适应步长的自然梯度算法,根据实际分离度自动调整步长,通过设置两个参数作为分离度系数控制收敛速率和稳态误差。仿真实验结果表明相较固定步长算法,该算法具有较好的分离性能,收敛速率提高了1倍,同时稳态误差缩小了3倍。
To solve the contradiction that fixed step size natural gradient algorithm brings between convergence rate and steady- state error, a natural gradient algorithm based on adaptive step size is adopted that step size changes automatically depending on separation degree, and two parameters are used as the coefficient of separation degree to control separate stability and convergence. Simulation results demonstrate that the algorithm has good separation property compared with fixed step size algorithm. Conver- gence rate doubles, and steady-state error narrows three times.
出处
《计算机工程与应用》
CSCD
2013年第21期216-219,共4页
Computer Engineering and Applications
基金
国家自然科学基金(No.61075008)
关键词
盲源分离
语音分离
自适应步长
分离度
收敛速率
稳态误差
blind source separation
speech separation
adaptive step size
separation degree
convergence rate
steaty-state error
