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.
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Kawaguchi, M., Ishii, N., Umeno, M. (2013). Analog Learning Neural Network Using Two-Stage Mode by Multiple and Sample Hold Circuits. In: Lee, R. (eds) Computer and Information Science. Studies in Computational Intelligence, vol 493. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00804-2_12
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DOI: https://doi.org/10.1007/978-3-319-00804-2_12
Publisher Name: Springer, Heidelberg
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