Using Machine Learning for Identification of Nonlinear Partially Observable Dynamic Systems.
Abstract
Abstract not provided.
- Authors:
- Publication Date:
- Research Org.:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1464692
- Report Number(s):
- SAND2017-8435C
656050
- DOE Contract Number:
- AC04-94AL85000
- Resource Type:
- Conference
- Resource Relation:
- Conference: Proposed for presentation at the Sandia Machine Learning and Deep Learning (MLDL) Conference held August 8-9, 2017 in Albuquerque, New Mexico.
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Gomez, Antonio, and Shia, Angie. Using Machine Learning for Identification of Nonlinear Partially Observable Dynamic Systems.. United States: N. p., 2017.
Web.
Gomez, Antonio, & Shia, Angie. Using Machine Learning for Identification of Nonlinear Partially Observable Dynamic Systems.. United States.
Gomez, Antonio, and Shia, Angie. 2017.
"Using Machine Learning for Identification of Nonlinear Partially Observable Dynamic Systems.". United States. https://www.osti.gov/servlets/purl/1464692.
@article{osti_1464692,
title = {Using Machine Learning for Identification of Nonlinear Partially Observable Dynamic Systems.},
author = {Gomez, Antonio and Shia, Angie},
abstractNote = {Abstract not provided.},
doi = {},
url = {https://www.osti.gov/biblio/1464692},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Aug 01 00:00:00 EDT 2017},
month = {Tue Aug 01 00:00:00 EDT 2017}
}
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