• 学术论文 •

### 一种基于峭度累积量比例微分控制的盲源分离学习率

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

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.