Estimation of all-terminal network reliability using an artificial neural network
Computers and Operations Research
International Journal of Computer Integrated Manufacturing
A neural network model for evaluating mobile ad hoc wireless network survivability
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
A data clustering algorithm for stratified data partitioning in artificial neural network
Expert Systems with Applications: An International Journal
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Neural networks must be constructed and validated with strong empirical dependence, which is difficult under conditions of sparse data. The paper examines the most common methods of neural network validation along with several general validation methods from the statistical resampling literature, as applied to function approximation networks with small sample sizes. It is shown that an increase in computation, necessary for the statistical resampling methods, produces networks that perform better than those constructed in the traditional manner. The statistical resampling methods also result in lower variance of validation, however some of the methods are biased in estimating network error