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Cited by 229

The DOI matches its Crossref record.

Confidence interval prediction for neural network models

Chryssolouris, G., Lee, M., Ramsey, A.·1996·IEEE Transactions on Neural Networks
DOI 10.1109/72.478409
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Abstract

To derive an estimate of a neural network's accuracy as an empirical modeling tool, a method to quantify the confidence intervals of a neural network model of a physical system is desired. In general, a model of a physical system has error associated with its predictions due to the dependence of the physical system's output on uncontrollable or unobservable quantities. A confidence interval can be computed for a neural network model with the assumption of normally distributed error for the neural network. The proposed method accounts for the accuracy of the data with which the neural network model is trained.

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Metadata

Authors
Chryssolouris, G., Lee, M., & Ramsey, A.
Venue
IEEE Transactions on Neural Networks
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
1996
Volume
7
Issue
1
Pages
229-232
ISSN
1045-9227 (ISSN-L)
DOI
10.1109/72.478409
OpenAlex
W2107376597
Cited by
229
References
0
FWCI
6.25
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Confidence interval prediction for neural network models. https://app.cochat.ai/papers/10.1109/72.478409
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Chryssolouris, G., Lee, M., & Ramsey, A. (1996). Confidence interval prediction for neural network models. IEEE Transactions on Neural Networks. https://doi.org/10.1109/72.478409

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.