Training an Artificial Neural Network with Op-amp Integrators Based Analog Circuits

被引：0

作者：

Liu, Weiliang ^{[1
]}

Li, Zhan ^{[1
,2
]}

Xue, Shengri ^{[1
]}

Yang, Xuebo ^{[1
]}

Lin, Weiyang ^{[1
,2
]}

机构：

[1] Harbin Inst Technol, Res Inst Intelligent Control & Syst, Harbin 150001, Heilongjiang, Peoples R China

[2] Harbin Inst Technol, Minist Educ, Key Lab Microsyst & Microstruct Mfg, Harbin 150001, Heilongjiang, Peoples R China

来源：

2018 IEEE CSAA GUIDANCE, NAVIGATION AND CONTROL CONFERENCE (CGNCC) | 2018年

基金：

黑龙江省自然科学基金; 中国博士后科学基金; 中国国家自然科学基金;

关键词：

IMPLEMENTATION; FPGA;

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：

The artificial neural network (ANN) has shown the effectiveness in unmanned systems and many other fields. Training an ANN with CPUs or other digital circuits can take a long time. And thus some analog neural network models have been composed to utilize the inherent advantage of analog circuits. However there are some problems in these models when considering about the flexibility and the speed. In this paper, we use op-amp integrators to store the weights of an analog ANN, and use the feedback of the circuit to train the network. It is easy to hold or change the weights in our circuit and the training of a sample can be automatically done quickly. The circuit only supports the ANN with one output in this paper. Besides more possible utilizations of the circuit are also proposed.

引用

页数：6

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