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Fault-tolerant training for optimal interpolative nets.

作者信息

Simon D, El-Sherief H

机构信息

TRW Syst. Integration Group, San Bernardino, CA.

出版信息

IEEE Trans Neural Netw. 1995;6(6):1531-5. doi: 10.1109/72.471356.

DOI:10.1109/72.471356
PMID:18263446
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.

摘要

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