双通道和双反回路自学记忆神经网络电路及其应用
Qiuzhen Wan1, Jiong Liu1, Tieqiao Liu2
1College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China.
概括
这项研究为埃尔曼神经网络引入了一种新型的自我学习记忆电路. 拟议的硬件加速学习,提高图像识别任务的准确性.
科学领域:
- 人工神经网络的硬件实现
- 记忆式计算电路的使用.
- 神经形态工程的神经形态工程
背景情况:
- 艾尔曼神经网络提供了对工程应用至关重要的局部内存.
- 之前的研究缺乏对Elman神经网络的硬件电路实现.
研究的目的:
- 为埃尔曼神经网络设计和实施自学记忆电路.
- 为了提高学习速度,并抑制过度学习.
- 将电路应用于图像识别任务.
主要方法:
- 提出了一个单一的神经网络模型,并设计了一个具有双反循环的自我学习记忆电路.
- 将Hebbian学习规则集成到全球反循环中,以防止过度学习.
- 开发了用于图像识别的多重神经网络电路.
主要成果:
- 当地反循环显著加快了自我学习过程.
- 升级的电路成功地抑制了对外学习.
- 多重神经网络电路实现了数字和灰度图像的快速准确识别.
结论:
- 拟议的双通道,双反循环自学记忆电路是埃尔曼神经网络的可行硬件实现.
- 这项工作展示了使用记忆神经形态电路进行高效和准确的图像识别的潜力.
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