DocumentCode
1905191
Title
A fault tolerant optimal interpolative net
Author
Simon, Dan ; El-Sherief, Hossny
Author_Institution
TRW Syst. Integration Group, San Bernardino, CA, USA
fYear
1993
fDate
1993
Firstpage
825
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 format, allowing for relatively short training times. A simulated fault tolerant OI Net is tested on a navigation satellite selective problem
Keywords
interpolation; learning (artificial intelligence); neural nets; pattern recognition; reliability; classification network; fault tolerant optimal interpolative net; recursive learning algorithm; Biological systems; Fault tolerance; Fault tolerant systems; Neural networks; Neurons; Prototypes; Robustness; Satellite navigation systems; Testing; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1993., IEEE International Conference on
Conference_Location
San Francisco, CA
Print_ISBN
0-7803-0999-5
Type
conf
DOI
10.1109/ICNN.1993.298665
Filename
298665
Link To Document