Simultaneous perturbation learning rule for Hopfield neural network

被引：0

作者：

Itonaga, S ^{[1
]}

Maeda, Y ^{[1
]}

机构：

[1] Kansai Univ, Dept Elect Engn, Suita, Osaka 5658680, Japan

来源：

KNOWLEDGE-BASED INTELLIGENT INFORMATION ENGINEERING SYSTEMS & ALLIED TECHNOLOGIES, PTS 1 AND 2 | 2001年 / 69卷

关键词：

Hopfield neural network; simultaneous perturbation learning rule;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Hopfield neural network (HNN) is a recurrent neural network proposed by J.J.Hopfield in 1984, The network has a characteristic of the association patterns ahead of time[1]. Originally, the weights of HNN are calculated by patterns to be memorized, then learning rule is not essential[2]. In this paper, we propose a recursive learning method for weight values in HNNs, which gives an optimal weights corresponding to a patterns to be memorized. The learning rule is based on the SP method [3][4][5][6].

引用

页码：171 / 174

页数：4

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