The Two-Stage Analog Neural Network Model and Hardware Implementation | IEEE Conference Publication | IEEE Xplore

The Two-Stage Analog Neural Network Model and Hardware Implementation

Publisher: IEEE

Abstract:

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...View more

Abstract:

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.
Date of Conference: 31 August 2014 - 04 September 2014
Date Added to IEEE Xplore: 01 December 2014
ISBN Information:
Publisher: IEEE
Conference Location: Kokura, Japan

I. Introduction

We propose the dynamic learning of the neural network by analog electronic circuits. This model will develop a new signal device with the analog neural electronic circuit. One of the targets of this research is the modeling of biomedical neural function. In the field of neural network, many application models have been proposed. And there are many hardware models that have been realized. These analog neural network models were composed of the operational amplifier and fixed resistance. It is difficult to change the connection coefficient.

References

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