The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Sep. 17, 2019

Filed:

Dec. 30, 2016
Applicant:

General Electric Company, Schenectady, NY (US);

Inventor:

Stephen William Piche, Austin, TX (US);

Assignee:

General Electric Company, Schenectady, NY (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G05B 13/00 (2006.01); G05B 13/04 (2006.01); G05B 13/02 (2006.01); G06N 20/00 (2019.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); G06F 17/16 (2006.01); G06N 7/00 (2006.01); G06F 17/11 (2006.01); G06F 17/50 (2006.01);
U.S. Cl.
CPC ...
G05B 13/041 (2013.01); G05B 13/027 (2013.01); G05B 13/0265 (2013.01); G06F 17/11 (2013.01); G06F 17/16 (2013.01); G06F 17/5009 (2013.01); G06F 17/5018 (2013.01); G06N 3/0445 (2013.01); G06N 3/0472 (2013.01); G06N 3/08 (2013.01); G06N 3/084 (2013.01); G06N 7/005 (2013.01); G06N 20/00 (2019.01); G06F 2217/16 (2013.01);
Abstract

A method for training a disturbance rejection model that is configured to model an operation of a system so to calculate a predicted value for a system output at a future time. The disturbance rejection model may include a network for mapping system inputs to the system output, the network including a weight vector and a feedback coefficient. The method may include: obtaining a training dataset, and training the disturbance rejection model pursuant to the training dataset. The training may include calculating updated values for each of the weight vector and the feedback coefficient of the network by minimizing an error function that include a first hyperparameter and a second hyperparameter. The first hyperparameter may include a vector for penalizing the weight vector and the second hyperparameter may include a scalar.


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