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Title: 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}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

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