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Robust analog neural network based on continuous valued number system

This paper explores properties of an analog artificial neural network architecture based on Continuous Valued Number System (CVNS) with distributed neurons. In conventional lumped neural networks, the effect of weight quantization errors effects the performance of the network as the network size increases. However, based on a stochastic model it is shown here that the CVNS capability in detecting and correcting errors along with the inherent self-scaling property of distributed neurons, can control the output quantization noise to signal ratio. This property contributes to a robust analog VLSI architecture based on analog distributed CVNS-Adaline neurons.