电子学报 ›› 2015, Vol. 43 ›› Issue (5): 929-934.DOI: 10.3969/j.issn.0372-2112.2015.05.015

• 学术论文 • 上一篇    下一篇

一种基于峭度累积量比例微分控制的盲源分离学习率

陈国钦   

  1. 福建师范大学福清分校电子与信息工程学院, 福建福清 350300
  • 收稿日期:2014-07-02 修回日期:2014-10-29 出版日期:2015-05-25
    • 作者简介:
    • 陈国钦 男,1962年生于福建永春.现为福建师范大学福清分校电子与信息工程学院副教授.主要研究为方向电子、通信和信号处理.E-mail:cgq6203@163.com
    • 基金资助:
    • 福建省教育厅A类重点项目 (No.JA13341)

A Learning Rate in Blind Source Separation Based on Proportional Differential Control of Kurtosis Cumulative

CHEN Guo-qin   

  1. School of Electronic and Information Engineering, Fuqing Branch of Fujian Normal University, Fuqing, Fujian 350300, China
  • Received:2014-07-02 Revised:2014-10-29 Online:2015-05-25 Published:2015-05-25

摘要:

自然梯度算法由于良好的分离性能在盲源分离中占有重要的地位,但该算法基于固定步长时,无法很好兼顾收敛速度和稳态误差.本文借鉴自动化控制的PID(Proportion Integration Differentiation)算法,提出一种与分离状态紧密结合的变步长学习率算法.由于完成分离的信号峭度累积量是一个固有值,分离过程的信号峭度累积量与固有值将有一个不断减小的误差值.该算法以指数函数值来体现该误差值.再利用该误差构成比例微分的变步长算法,其中的步长初始值就相当于控制误差的比例值,而误差的微分项则得到加速的调整值.该算法仿真实验结果与固定步长自然梯度盲源分离算法的仿真实验结果对比:对应于初始步长的一个最大值和一个最小值,该算法的两次迭代次数均低于采用固定步长算法的迭代次数,并且对于不同类型信号在两次迭代次数间的差值约10~40次,而两种算法的稳态误差是相同的.

关键词: 盲信号分离, 峭度累积, 比例微分控制, 变步长学习率

Abstract:

Natural gradient algorithm occupies an important position in blind source separation due to its good separation performance,but when the algorithm is based on a fixed-step size,a good balance will impossibly be achieved between the convergence rate and steady-state error.This article drew PID (Proportion Integration Differentiation) algorithm of automation control as a reference and proposed an algorithms in variable-step learning rate closely integrated with the state of separation.Due to the fact the cumulative amount of the signal kurtosis was an intrinsic value after the complete separation,there arose a gradually decreasing error value between the cumulative amount of the signal kurtosis of the separation process and the inherent value.The exponential function value of e in the algorithm was applied to reflect the error value.Then the error was used to constitute proportional differential variable-step algorithm,among which the initial value of the step was equivalent to proportional value of the error control,and the differential term of the error gained the adjusted values in acceleration.The simulation results show that corresponding to a maximum and a minimum of step initial value,the number of iterations of the algorithm in two times was lower than that of iterations with fixed-step algorithm,and the difference between the two iterations was about 10 to 40 times for signals of different type,however,the steady-state error of the two algorithms was the same.

Key words: blind signal separation, kurtosis cumulative, proportion differential control, variable-step size in learning rate

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