Document Type
Article
Publication Date
11-1995
Publication Title
IEEE Transactions on Neural Networks
Abstract
The optimal interpolative (OI) classification network is extended to include fault tolerance and make the network more robust to the loss of a neuron. The OI net has the characteristic that the training data are fit with no more neurons than necessary. Fault tolerance further reduces the number of neurons generated during the learning procedure while maintaining the generalization capabilities of the network. The learning algorithm for the fault-tolerant OI net is presented in a recursive formal, allowing for relatively short training times. A simulated fault-tolerant OI net is tested on a navigation satellite selection problem
Repository Citation
Simon, Daniel J. and El-Sherief, Hossny, "Fault Tolerant Training for Optimal Interpolative Nets" (1995). Electrical and Computer Engineering Faculty Publications. 153.
https://engagedscholarship.csuohio.edu/enece_facpub/153
Original Citation
D. Simon and H. El-Sherief. (1995). Fault Tolerant Training for Optimal Interpolative Nets, IEEE Transactions on Neural Networks, 6(6), 1531-1535, doi: 10.1109/72.471356.
DOI
10.1109/72.471356
Version
Postprint
Publisher's Statement
© 1995 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Volume
6
Issue
6
