Analog Learning Neural Network using Two-Stage Mode by Multiple and Sample Hold Circuits

Publisher: IGI Global_journal

E-ISSN: 2166-7179|2|1|61-72

ISSN: 2166-7160

Source: International Journal of Software Innovation (IJSI), Vol.2, Iss.1, 2014-01, pp. : 61-72

Disclaimer: Any content in publications that violate the sovereignty, the constitution or regulations of the PRC is not accepted or approved by CNPIEC.

Previous Menu Next

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.