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Comparative fault tolerance of parallel distributed processing networks

Content Provider	IEEE Xplore Digital Library
Author	Segee, B.E. Carter, M.J.
Copyright Year	1968
Abstract	We propose a method for evaluating and comparing the fault tolerance of a wide variety of parallel distributed processing networks (more commonly referred to as artificial neural networks). Despite the fact that these computing networks are biologically inspired and share many features of biological neural networks, they are not inherently tolerant of the loss of processing elements. We examine two classes of networks, multilayer perceptrons and Gaussian radial basis function networks, and show that there is a marked difference in their operational fault tolerance. Furthermore, we show that fault tolerance is influenced by the training algorithm used and even the initial state of the network. Using an idea due to Sequin and Clay (1990), we show that training with intermittent, randomly selected faults can dramatically enhance the fault tolerance of radial basis function networks, while it yields only marginal improvement when used with multilayer perceptrons.<>
Sponsorship	IEEE Computer Society Technical Committee on Distributed Process IEEE Computer Society Technical Committee on VLSI IEEE Technical Committee on Computer Architecture IEEE Computer Society
Starting Page	1323
Ending Page	1329
Page Count	7
File Size	808945
File Format	PDF
ISSN	00189340
Volume Number	43
Issue Number	11
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-11-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault tolerance Distributed processing Routing Delay effects Very large scale integration Hardware Computer networks Parallel processing Clocks Communication switching
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computational Theory and Mathematics Software Hardware and Architecture

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