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:
Dec. 28, 2021

Filed:

Aug. 23, 2019
Applicant:

Hrl Laboratories, Llc, Malibu, CA (US);

Inventors:

Soheil Kolouri, Agoura Hills, CA (US);

Nicholas A. Ketz, Madison, WI (US);

Praveen K. Pilly, Tarzana, CA (US);

Charles E. Martin, Thousand Oaks, CA (US);

Michael D. Howard, Westlake Village, CA (US);

Assignee:

HRL Laboratories, LLC, Malibu, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/62 (2006.01); G06N 3/08 (2006.01); G06N 3/04 (2006.01); G06K 9/00 (2006.01); G06K 9/72 (2006.01);
U.S. Cl.
CPC ...
G06K 9/6256 (2013.01); G06K 9/00805 (2013.01); G06K 9/6262 (2013.01); G06K 9/6267 (2013.01); G06N 3/04 (2013.01); G06N 3/084 (2013.01); G06K 9/726 (2013.01);
Abstract

An autonomous navigation system for a vehicle includes a controller configured to control the vehicle, sensors configured to detect objects in a path of the vehicle, nonvolatile memory including an artificial neural network configured to classify the objects detected by the sensors, and a processor. The artificial neural network includes a series of neurons in each of an input layer, at least one hidden layer, and an output layer. The memory includes instructions which, when executed by the processor, cause the processor to train the artificial neural network on a first task, identify, utilizing a contrastive excitation backpropagation algorithm, important neurons for the first task, identify, utilizing a learning algorithm, important synapses between the neurons for the first task based on the important neurons identified, and rigidify the important synapses to achieve selective plasticity of the series of neurons in the artificial neural network.


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