吉林大学学报(理学版)

• 数学 • 上一篇    下一篇

基于典型相关性分析的自适应拟牛顿盲源分离算法

张瑞芬, 冶继民   

  1. 西安电子科技大学 数学与统计学院, 西安 710071
  • 收稿日期:2016-10-20 出版日期:2017-05-26 发布日期:2017-05-31
  • 通讯作者: 冶继民 E-mail:jmye@mail.xidian.edu.cn

Adaptive QuasiNewton Algorithm for Blind Source Separation Based on Canonical Correlation Analysis

ZHANG Ruifen, YE Jimin   

  1. School of Mathematics and Statistics, Xidian University, Xi’an 710071, China
  • Received:2016-10-20 Online:2017-05-26 Published:2017-05-31
  • Contact: YE Jimin E-mail:jmye@mail.xidian.edu.cn

摘要: 通过分析经典的典型相关性分析标准, 提出一种新的源信号抽取标准, 并利用在线拟牛顿算法求解新标准, 进而得到一种新的基于典型相关性分析的盲源信号抽取算法, 实现了盲源分离. 理论分析表明, 新源信号抽取标准的唯一全局最小值点是经典典型相关性分析标准的最大值点. 仿真结果表明, 新算法收敛速度更快.

关键词: 盲源分离, 拟牛顿迭代, 典型相关性分析

Abstract: By the analysis of classical canonical correlation analysis criterion, we proposed a new criterion for source signal extraction. Using the online quasiNewton iteration algorithm to solve the new criterion, and then a new blind source extraction algorithm based on canonical correlation analysis was obtained and blind source separation was realized. Theoretical analysis show that the unique global minimum point of the new source signal extraction criterion is the maximum point of classical canonical correlation analysis criterion. Simulation results show that the convergence speed the new algorithm is faster.

Key words:  blind source separation, canonical correlation analysis, quasiNewton iteration

中图分类号: 

  • O213