Uncertainty in Artificial Neural Network Models: Monte-Carlo Simulations Beyond the GUM Boundaries-Reference-Cited by-同舟云学术

Uncertainty in Artificial Neural Network Models: Monte-Carlo Simulations Beyond the GUM Boundaries

Published:2024-02-29 Issue:2 Volume:22 Page:141-159
ISSN:1536-6367
Container-title:Measurement: Interdisciplinary Research and Perspectives
language:en
Short-container-title:Measurement: Interdisciplinary Research and Perspectives

Author:

Sadek A.M.¹²³^ORCID,Al-Muhlaki Fahad²

Affiliation:

1. General Administration of Laboratories, Saudi Standards Metrology and Quality Organization (SASO), Riyadh, Kingdom of Saudi Arabia

2. National Measurement and Calibration Center (NMCC), Saudi Standard Metrology and Quality Organization (SASO), Riyadh, Kingdom of Saudi Arabia

3. Ionizing Radiation Metrology Department, National Institute for Standards, Giza, Egypt

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/15366367.2023.2246112

Reference45 articles.

1. Adamu, A., Maul, T., & Bargiela, A. (2013). On training neural networks with transfer function diversity. In International Conference on Computing Intelligence and Information Technology, CIIT 13, Mumbai, India. Elsiver.

2. The Effect of the Normalization Method Used in Different Sample Sizes on the Success of Artificial Neural Network Model

3. DEVDAN: Deep evolving denoising autoencoder

4. Four Generations of High-Dimensional Neural Network Potentials

5. Neural Networks

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