两阶段模拟神经网络模型及硬件实现

M. Kawaguchi, M. Umeno, N. Ishii
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引用次数: 2

摘要

在神经网络领域,已经提出了许多应用模型。为实现生物医学视觉系统的神经网络模型仿真,研制了神经芯片和人工视网膜芯片。以往的模拟神经网络模型是由运算放大器和固定电阻组成的。连接系数很难改变。在本研究中,我们使用了模拟电子多路和采样保持电路。连接权重描述输入电压。连接系数很容易改变。这个模型只适用于模拟电路。它可以在很短的时间内完成学习过程,使学习更加灵活。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Two-Stage Analog Neural Network Model and Hardware Implementation
In the neural network field, many application models have been proposed. A neuro chip and an artificial retina chip are developed to comprise the neural network model and simulate the biomedical vision system. Previous analog neural network models were composed of the operational amplifier and fixed resistance. It is difficult to change the connection coefficient. In this study, we used analog electronic multiple and sample hold circuits. The connecting weights describe the input voltage. It is easy to change the connection coefficient. This model works only on analog electronic circuits. It can finish the learning process in a very short time and this model will enable more flexible learning.
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