Interpretable Machine Learning for Function Approximation in Structural Health Monitoring-Reference-Cited by-同舟云学术

Interpretable Machine Learning for Function Approximation in Structural Health Monitoring

Published:2021-10-24 Issue: Volume: Page:369-388
ISSN:2522-560X
Container-title:Structural Integrity
language:
Short-container-title:

Author:

Pei Jin-Song,Hougen Dean F.,Kanneganti Sai Teja,Wright Joseph P.,Mai Eric C.,Smyth Andrew W.,Masri Sami F.,Derkevorkian Armen,Gay-Balmaz François,Komini Ludian

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-81716-9_18

Reference54 articles.

1. Amari SI, Park H, Ozeki T (2006) Singularities affect dynamics of learning in neuromanifolds. Neural Comput 18:1007–1065

2. Barron AR (1993) Universal approximation bounds for superpositions of a sigmoidal function. IEEE Trans Inf Theory 39(3):930–945

3. Barron AR (1994) Approximation and estimation bounds for artificial neural networks. Mach Learn 14:115–133

4. Blum AL, Rivest RL (1995) Training a 3-node neural network is NP-complete. Neural Netw 5:117–127

5. Brewick PT, Abdelbarr M, Derkevorkian A, Kolaini AR, Masri SF, Pei JS (2018) Fusion of state-space modeling and data-driven strategies for computational shock response prediction. The American Institute of Aeronautics and Astronautics (AIAA) Journal Published online on 28 Feb 2018

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Theoretical Insights into Neural Networks and Deep Learning: Advancing Understanding, Interpretability, and Generalization;2023 World Conference on Communication & Computing (WCONF);2023-07-14