FPGA implementation of hopfield neural network via simultaneous perturbation rule

被引：0
|
作者：
Wakamura, M [1 ]
Maeda, Y [1 ]
机构：
[1] Kansai Univ, Dept Elect Engn, Suita, Osaka 5648680, Japan
来源：
SICE 2003 ANNUAL CONFERENCE, VOLS 1-3 | 2003年
关键词：
implementation; simultaneous perturbation learning rule; Hopfield Neural;
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 typical example of recurrent neural networks. Software implementation of HNN does not obtain sufficient speed in the operation. Therefore, hardware implementation, especially, FPGA implementation of HNN is very promising. Originally, the weights of HNN are. calculated by patterns to be memorized. However, we adopt a recursive learning method via the simultaneous perturbation learning rule.
引用
收藏
页码：1272 / 1275
页数：4
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