Resistive-type CVNS distributed neural networks with improved noise-to-signal ratio

Authors:
Golnar Khodabandehloo;Mitra Mirhassani;Majid Ahmadi
Affiliations:
Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, Canada;Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, Canada;Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, Canada
Venue:
IEEE Transactions on Circuits and Systems II: Express Briefs
Year:
2010

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Abstract

Resistive-type distributed neural networks (DNNs) provide a self-scaling structure for the neuron, which can spontaneously adapt itself to different numbers of inputs. In lumped neural networks, the neuron should be changed whenever the number of inputs changes due to the applications; redesigning the neuron is not practical, particularly for hardware implementations. In this brief, a group of feedforward DNNs based on a continuous valued number system is proposed, which outperforms not only the lumped neural networks but also the conventional DNNs because of the reduced sensitivity to noise.