Data-Driven Priors for Robust PSSE via Gauss-Newton Unrolled Neural Networks-Reference-Cited by-同舟云学术

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Data-Driven Priors for Robust PSSE via Gauss-Newton Unrolled Neural Networks

Published:2022-03 Issue:1 Volume:12 Page:172-181
ISSN:2156-3357
Container-title:IEEE Journal on Emerging and Selected Topics in Circuits and Systems
language:
Short-container-title:IEEE J. Emerg. Sel. Topics Circuits Syst.

Author:

Yang Qiuling¹^ORCID,Sadeghi Alireza²^ORCID,Wang Gang¹^ORCID

Affiliation:

1. State Key Laboratory of Intelligent Control and Decision of Complex Systems and the School of Automation, Beijing Institute of Technology, Beijing, China

2. Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

https://ieeexplore.ieee.org/ielam/5503868/9737325/9676571-aam.pdf

Reference42 articles.

1. Quantifying Distributional Model Risk via Optimal Transport

2. Certifying some distributional robustness with principled adversarial training;sinha;arXiv 1710 10571,2017

3. Learning ReLU Networks on Linearly Separable Data: Algorithm, Optimality, and Generalization

4. Adaptive subgradient methods for online learning and stochastic optimization;duchi;J Mach Learn Res,2011

5. Adam: A method for stochastic optimization;kingma;arXiv 1412 6980,2014

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