The Analysis of the Faulty Behavior of Synchronous Neural Networks

Authors:
Lee A. Belfore, II;Barry W. Johnson
Affiliations:
-;-
Venue:
IEEE Transactions on Computers - Special issue on artificial neural networks
Year:
1991

Citing 4
Cited 1

Design & analysis of fault tolerant digital systems

Design & analysis of fault tolerant digital systems
Learning and relearning in Boltzmann machines

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Resource requirements of standard and programmable nets

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Introduction to probabilistic automata (Computer science and applied mathematics)

Introduction to probabilistic automata (Computer science and applied mathematics)

The faulty behavior of feedforward neural networks with hard-limiting activation function

Neural Computation

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Abstract

A means for analyzing the faulty behavior of neural networks is presented. Using an analogy between statistical physics and neural networks, a method for assessing the performance of a synchronous neural network model in the presence of faults is developed. Analytical predictions are computed using the statistical physics analogy and compared with the simulated behavior for two neuron models. An example of the analytical technique applied to an autoassociative memory is described.