基于Hebbian规则的新型自适应广义主元分析算法

高迎彬; 孔祥玉; 崔巧花; 董海迪

doi:10.11959/j.issn.1000-436x.2020134

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基于Hebbian规则的新型自适应广义主元分析算法

学术论文 | 更新时间：2024-06-05

- 基于Hebbian规则的新型自适应广义主元分析算法
- Novel adaptive generalized principal component analysis algorithm based on Hebbian rule
- 通信学报 2020年41卷第7期页码：103-109
- 作者机构：
  
  1. 中国电子科技集团公司第五十四研究所，河北石家庄 050081
  2. 火箭军工程大学导弹工程学院，陕西西安 710025
  3. 海军工程大学兵器工程学院，湖北武汉 430033
- 作者简介：
  
  [ "高迎彬（1986- ），男，河北安平人，博士，中国电子科技集团公司第五十四研究所工程师，主要研究方向为自适应信号处理和阵列信号处理等" ]
  [ "孔祥玉（1967- ），男，山西临汾人，博士，火箭军工程大学教授、博士生导师，主要研究方向为自适应信号处理、故障诊断和特征提取等" ]
  [ "崔巧花（1987- ），女，河北邱县人，中国电子科技集团公司第五十四研究所工程师，主要研究方向为散射通信等" ]
  [ "董海迪（1989- ），男，湖北武汉人，博士，海军工程大学讲师，主要研究方向为自适应信号处理和故障诊断等" ]
- 基金信息：
  
  国家自然科学基金资助项目(61833016);国家自然科学基金资助项目(61673387);国家自然科学基金资助项目(61374120);陕西省自然科学基金资助项目(2020JM-356)
- DOI：10.11959/j.issn.1000-436x.2020134
  中图分类号： TP391
- 网络出版日期：2020-07，
  
  纸质出版日期：2020-07-25
- 稿件说明：
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高迎彬, 孔祥玉, 崔巧花, 等. 基于Hebbian规则的新型自适应广义主元分析算法[J]. 通信学报, 2020,41(7):103-109.

Yingbin GAO, Xiangyu KONG, Qiaohua CUI, et al. Novel adaptive generalized principal component analysis algorithm based on Hebbian rule[J]. Journal on communications, 2020, 41(7): 103-109.
高迎彬, 孔祥玉, 崔巧花, 等. 基于Hebbian规则的新型自适应广义主元分析算法[J]. 通信学报, 2020,41(7):103-109. DOI： 10.11959/j.issn.1000-436x.2020134.

Yingbin GAO, Xiangyu KONG, Qiaohua CUI, et al. Novel adaptive generalized principal component analysis algorithm based on Hebbian rule[J]. Journal on communications, 2020, 41(7): 103-109. DOI： 10.11959/j.issn.1000-436x.2020134.

摘要

为了从输入信号中自适应地对信号的广义主元进行估计，基于Hebbian线性神经元模型，提出了一种新型广义主元分析算法。该算法通过当前时刻的采样值来估计信号的自相关矩阵，有效地降低了算法的计算复杂度。利用 Lyapunov 稳定性定理进行平衡点分析表明：当且仅当神经元权向量收敛到信号的广义主元时，算法到达稳定状态。仿真实验表明：相比一些同类型算法，所提算法具有较快的收敛速度。

Abstract

In order to adaptively estimate the generalized principal component from input signals

a novel generalized principal component analysis algorithm was proposed based on the Hebbian linear neuron model.Since the autocorrelation matrices of the signals were estimated directly from the sampled data at the current time

the proposed algorithm had low computation complexity.Trough analyzing all of the equilibrium points by Lyapunov method

it is proven that if and only if the weight vector in the neuron had the same direction with the generalized principal component

the proposed algorithm attains the convergence status.Simulation results shows that compared with some same type algorithms

the proposed algorithm has faster convergence speed.

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references

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