A Learning Algorithm of Associative Memory with Optimal Stability of Pattern Components

梁学斌; 吴立德

您当前的位置：

首页 >

文章列表页 >

A Learning Algorithm of Associative Memory with Optimal Stability of Pattern Components

更新时间：2025-12-08

- A Learning Algorithm of Associative Memory with Optimal Stability of Pattern Components
- Acta Electronica Sinica Issue 5, (1996)
- 作者机构：
  
  复旦大学计算机科学系
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP18
- Published：1996
- 稿件说明：
移动端阅览
[1]梁学斌,吴立德.基于模式分量最优稳定性准则的联想记忆学习算法[J].电子学报,1996(05):30-33.

梁学斌, 吴立德. A Learning Algorithm of Associative Memory with Optimal Stability of Pattern Components[J]. Acta Electronica Sinica, 1996, (5).
[1]梁学斌,吴立德.基于模式分量最优稳定性准则的联想记忆学习算法[J].电子学报,1996(05):30-33. DOI：

梁学斌, 吴立德. A Learning Algorithm of Associative Memory with Optimal Stability of Pattern Components[J]. Acta Electronica Sinica, 1996, (5). DOI：

摘要

提出了设计联想记忆神经网络的模式分量最优稳定性准则，即使得每个模式分量对于模式畸变都具有最大的联想容错性，基于此，发展了一个约束感知器优化学习算法。它具有两个特点：（ｌ）可以全部存储任意给定的训练模式集；（２）每个模式分量都具有最大的联想容错性，实验结果表明，所提出的优化学习算法比现有算法具有更强的存储联想能力。

Abstract

The criterion which requires each pattern component to possess optimal stability is proposed. Based on the criterion

a constrained perceptron optimization algorithm is developed

which has the following two characteristics: （1） It can store any given training patterns set; （2）Each pattern component possesses optimal stability. Computer simulation results confirm the ad vantages of our optimization learning algorithm over existing ones.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Multiple Weighted Improved Exponential Bidirectional Associative Memory Model

A Study of Neural Networl Model with Importance Emphasizing for Associative Memory

A New Analytical Method for Hopfield Type Associative Memory Neural Networks

Cellular Neural Networks with Multi-Step Output Function

Fractal Cellular Neural Networks for Associative Memory

Related Author

CHEN Song-can

CAI Jun

Yu Daoheng & Yu Limin

于立民

Mlao Zhenjiang

Yuan Baozong

王俊生

甘强

Related Institution

南京航空航天大学计算机科学与工程系

南京大学计算机软件新技术国家重点实验室

南京航空航天大学计算机科学与工程系江苏南京

南京大学计算机软件新技术国家重点实验室江苏南京

Dept. of Radio Electronics, Peking University

⁰