A Note on the Complexity of Reliability in Neural Networks

Piotr Berman; Ian Parberry; Georg Schnitger

doi:10.1109/72.165601

A Note on the Complexity of Reliability in Neural Networks

Piotr Berman, Ian Parberry, Georg Schnitger

Research output: Contribution to journal › Article › peer-review

Abstract

It is shown that, in a standard discrete neural network model with small fan-in, tolerance to random malicious faults can be achieved with a log-linear increase in the number of neurons and a constant factor increase in parallel time, provided fan-in can increase arbitrarily. A similar result is obtained for a nonstandard but closely related model with no restriction on fan-in.

Original language	English (US)
Pages (from-to)	998-1002
Number of pages	5
Journal	IEEE Transactions on Neural Networks
Volume	3
Issue number	6
DOIs	https://doi.org/10.1109/72.165601
State	Published - Nov 1992

All Science Journal Classification (ASJC) codes

Software
Computer Science Applications
Computer Networks and Communications
Artificial Intelligence

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title = "A Note on the Complexity of Reliability in Neural Networks",

abstract = "It is shown that, in a standard discrete neural network model with small fan-in, tolerance to random malicious faults can be achieved with a log-linear increase in the number of neurons and a constant factor increase in parallel time, provided fan-in can increase arbitrarily. A similar result is obtained for a nonstandard but closely related model with no restriction on fan-in.",

author = "Piotr Berman and Ian Parberry and Georg Schnitger",

year = "1992",

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N2 - It is shown that, in a standard discrete neural network model with small fan-in, tolerance to random malicious faults can be achieved with a log-linear increase in the number of neurons and a constant factor increase in parallel time, provided fan-in can increase arbitrarily. A similar result is obtained for a nonstandard but closely related model with no restriction on fan-in.

AB - It is shown that, in a standard discrete neural network model with small fan-in, tolerance to random malicious faults can be achieved with a log-linear increase in the number of neurons and a constant factor increase in parallel time, provided fan-in can increase arbitrarily. A similar result is obtained for a nonstandard but closely related model with no restriction on fan-in.

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A Note on the Complexity of Reliability in Neural Networks

Abstract

All Science Journal Classification (ASJC) codes

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