Pulse density Hopfield Neural Network system with learning capability using FPGA

被引：0

作者：

Maeda, Yutaka ^{[1
]}

Fukuda, Yoshinori ^{[1
]}

机构：

[1] Kansai Univ, Dept Elect & Elect Engn, 3-3-35 Yamate Cho, Suita, Osaka 5648680, Japan

来源：

COMPUTATIONAL METHODS AND APPLIED COMPUTING | 2008年

关键词：

hardware implementation; pulse density; HNN; learning; simultaneous perturbation; FPGA;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this paper, we present a FPGA Hopfield Neural Network system with learning capability using the simultaneous perturbation learning rule. In the neural network, outputs and internal values are represented by pulse train. That is, analog Hopfield Neural Network with pulse frequency representation is considered. The pulse density representation and the simultaneous perturbation enable the system with learning capability to easily implement as a hardware system. Details of the design are described. Analog and digital examples are also shown to confirm a viability of the system configuration and the learning capability.

引用

页码：320 / +

页数：2

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