Accuracy vs. Precision in Digital VLSI Architectures for Signal Processing
IEEE Transactions on Computers
Sensitivity analysis of neocognitron
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Sensitivity analysis of multilayer perceptron to input and weight perturbations
IEEE Transactions on Neural Networks
The selection of weight accuracies for Madalines
IEEE Transactions on Neural Networks
Sensitivity analysis of single hidden-layer neural networks with threshold functions
IEEE Transactions on Neural Networks
Resistive-type CVNS distributed neural networks with improved noise-to-signal ratio
IEEE Transactions on Circuits and Systems II: Express Briefs
Analog implementation of a novel resistive-type sigmoidal neuron
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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In this article Continuous Valued Number System is studied as an alternative method for implementing Analog Neural Networks. Continuous Valued Number System is analog in nature and employs digit level analog modular arithmetic. The information redundancy among the digits allows efficient operations using analog circuitry with arbitrary accuracy. The general operations in this number system are more precise than regular analog operations, thus enabling us to implement large size analog neural networks with more precision. In this article, function evaluation properties of the Continuous Valued Number System are introduced. These key properties are used for developing analog Adaline with a nonlinear activation function. Stochastic modeling of a network of such elements is carried out which indicates that the proposed network has low sensitivity to implementation errors.