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

Access to Document

10.1109/72.165601

Cite this

@article{f5f4105112a840b2824d34b5510d9fbb,

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",

month = nov,

doi = "10.1109/72.165601",

language = "English (US)",

volume = "3",

pages = "998--1002",

journal = "IEEE Transactions on Neural Networks",

issn = "1045-9227",

publisher = "IEEE Computational Intelligence Society",

number = "6",

}

TY - JOUR

T1 - A Note on the Complexity of Reliability in Neural Networks

AU - Berman, Piotr

AU - Parberry, Ian

AU - Schnitger, Georg

PY - 1992/11

Y1 - 1992/11

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.

UR - https://www.scopus.com/pages/publications/0026954631

UR - https://www.scopus.com/pages/publications/0026954631#tab=citedBy

U2 - 10.1109/72.165601

DO - 10.1109/72.165601

M3 - Article

C2 - 18276496

AN - SCOPUS:0026954631

SN - 1045-9227

VL - 3

SP - 998

EP - 1002

JO - IEEE Transactions on Neural Networks

JF - IEEE Transactions on Neural Networks

IS - 6

ER -

A Note on the Complexity of Reliability in Neural Networks

Abstract

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this