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Author Elsimary, H. ♦ Mashali, S. ♦ Shaheen, S.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©1995
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations
Subject Keyword Fault tolerance ♦ Feeds ♦ Neural networks ♦ Feedforward neural networks ♦ Artificial neural networks ♦ Function approximation ♦ Training data ♦ Testing ♦ Design engineering ♦ Hardware
Abstract Obtaining the maximum generalization and fault tolerance has been an important issue in the design of feedforward artificial neural networks (FFANNs). In previous work we introduced a method for ensuring the fault tolerance capabilities of FFANNs. We also introduced a detached model for fault tolerance, this model was shown to be realistic and appropriate for emulating faults that arise in FFANNs hardware implementation. In this paper we discuss the generalization ability of the fault tolerant FFANNs produced by our new training method. By introducing a method for measuring the generalization ability, this works shows that the network trained by our method has better generalization ability than that trained by conventional backpropagation technique.
Description Author affiliation: Electron. Res. Inst., Cairo, Egypt (Elsimary, H.; Mashali, S.)
ISBN 0780325591
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1995-10-22
Publisher Place Canada
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 515.25 kB
Page Count 5
Starting Page 30
Ending Page 34


Source: IEEE Xplore Digital Library