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Author Ito, T. ♦ Takanami, I.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©1997
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations ♦ Applied physics
Subject Keyword Fault tolerance ♦ Neural networks ♦ Feedforward neural networks ♦ Neurons ♦ Multi-layer neural network ♦ Signal processing algorithms ♦ Artificial neural networks ♦ Wire ♦ Learning systems ♦ Indium tin oxide
Abstract To make a neural network fault-tolerant, Tan et al. proposed a learning algorithm which injects intentionally the snapping of a wire one by one into a network (1992, 1992, 1993). This paper proposes a learning algorithm that injects intentionally stuck-at faults to neurons. Then by computer simulations, we investigate the recognition rate in terms of the number of snapping faults and reliabilities of lines and the learning cycle. The results show that our method is more efficient and useful than the method of Tan et al. Furthermore, we investigate the internal structure in terms of ditribution of correlations between input values of a output neuron for the respective learning methods and show that there is a significant difference of the distributions among the methods.
Description Author affiliation: Dept. of Comput. Sci., Iwate Univ., Morioka, Japan (Ito, T.)
ISBN 0818682094
ISSN 10817735
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1997-11-17
Publisher Place Japan
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 532.52 kB
Page Count 6
Starting Page 88
Ending Page 93

Source: IEEE Xplore Digital Library