A Left-Right Neural Network for Singularvalue Decompositions of General Matrices

冯大政; 保铮; 焦李成

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A Left-Right Neural Network for Singularvalue Decompositions of General Matrices

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- A Left-Right Neural Network for Singularvalue Decompositions of General Matrices
- Acta Electronica Sinica Issue 12, (1995)
- 作者机构：
  
  西安电子科技大学电子工程研究所
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP18
- Published：1995
- 稿件说明：
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[1]冯大政,保铮,焦李成.用于一般矩阵奇异值分解的左、右神经网络[J].电子学报,1995(12):115-118.

冯大政, 保铮, 焦李成. A Left-Right Neural Network for Singularvalue Decompositions of General Matrices[J]. Acta Electronica Sinica, 1995, (12).
[1]冯大政,保铮,焦李成.用于一般矩阵奇异值分解的左、右神经网络[J].电子学报,1995(12):115-118. DOI：

冯大政, 保铮, 焦李成. A Left-Right Neural Network for Singularvalue Decompositions of General Matrices[J]. Acta Electronica Sinica, 1995, (12). DOI：

摘要

本文利用线性规划神经网络的结构和第一主分量分析神经网络的动态方程，提出一种左、右神经网络（ＬＲＮＮ），给出并证明了它的有界性和稳定性定理。加上反Ｈｅｂｂ权修正规则，ＬＲＮＮ可以进行奇异值分解（ＳＶＤ）和对称矩阵的特征值分解（ＥＶＤ）．从渐近收敛速度和仿真结果都说明，ＬＲＮＮ是非常有效的。

Abstract

Based on the structure of linear programming neural circuits and a simiplified neuron model as a principal component analyzer

a left-right neural network （LRNN） is proposed

and its boundedness and stability are given and proved. Both LRNN and its anti-Hebb rule can solve the singularvalue decomposition of the general matrices and the eigenvalue decomposition of the symmetric matrices. The related simulations are also given.

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