Dynamical Associative Memory: The Properties of the New Weighted Chaotic Adachi Neural Network

被引：0

作者：

Luo, Guangchun ^{[1
]}

Ren, Jinsheng ^{[1
]}

Qin, Ke ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Sichuan, Peoples R China

来源：

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | 2012年 / E95D卷 / 08期

关键词：

chaotic neural networks; chaotic pattern recognition; dynamical associative memory; Adachi Neural Network;

D O I：

10.1587/transinf.E95.D.2158

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A new training algorithm for the chaotic Adachi Neural Network (AdNN) is investigated. The classical training algorithm for the AdNN and it's variants is usually a "one-shot" learning, for example, the Outer Product Rule (OPR) is the most used. Although the OPR is effective for conventional neural networks, its effectiveness and adequateness for Chaotic Neural Networks (CNNs) have not been discussed formally. As a complementary and tentative work in this field, we modified the AdNN's weights by enforcing an unsupervised Hebbian rule. Experimental analysis shows that the new weighted AdNN yields even stronger dynamical associative memory and pattern recognition phenomena for different settings than the primitive AdNN.

引用

页码：2158 / 2162

页数：5

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