Chaotic Associative Memory with Adaptive Scaling Factor

被引：1

作者：

Okada, Tatsuuya ^{[1
]}

Osana, Yuko ^{[1
]}

机构：

[1] Tokyo Univ Technol, 1404-1 Katakura, Hachioji, Tokyo 1920982, Japan

来源：

ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, PT II | 2017年 / 10614卷

关键词：

Chatic associative memory; Dynamic association; Scaling factor; NEURAL-NETWORKS;

D O I：

10.1007/978-3-319-68612-7_81

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we propose a Chaotic Associative Memory with Adaptive Scaling Factor. In the proposed model, the scaling factor of refractoriness is adjusted according to the maximum absolute value of the internal state up to that time as similar as the conventional Chaotic Multidirectional Associative Memory with Adaptive Scaling Factor. Computer experiments are carried out and we confirmed that the proposed model has the same dynamic association ability as the conventional model, and the proposed model also has recall capability similar to that of the conventional model, even for the number of neurons not used for automatic adjustment of parameters.

引用

页码：713 / 721

页数：9

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