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:
May. 21, 2024

Filed:

Oct. 26, 2020
Applicant:

Jiangxi University of Science and Technology, Ganzhou, CN;

Inventors:

Xiaoyan Luo, Ganzhou, CN;

Hui Yu, Ganzhou, CN;

Tao Deng, Ganzhou, CN;

Junxi Liu, Ganzhou, CN;

Xuetao Zhang, Ganzhou, CN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 3/084 (2023.01); G06N 3/04 (2023.01); G06N 3/082 (2023.01); G06N 3/126 (2023.01); G06N 5/022 (2023.01); G10L 25/03 (2013.01); G10L 25/30 (2013.01); G10L 25/51 (2013.01);
U.S. Cl.
CPC ...
G06N 3/084 (2013.01); G06N 3/04 (2013.01); G06N 3/082 (2013.01); G06N 3/126 (2013.01); G06N 5/022 (2013.01); G10L 25/03 (2013.01); G10L 25/30 (2013.01); G10L 25/51 (2013.01);
Abstract

Embodiments of the present application provide a prediction method, device and system for rock mass instability stages, and belong to the technical field of rock mass instability prediction. The method includes the steps: acquiring acoustic emission signals of rock mass; extracting feature parameters from the acquired acoustic emission signals; and predicting instability stages of the rock mass in accordance with the feature parameters and a preset back propagation (BP) neural network model, wherein the preset BP neural network model is obtained by training a BP neural network and a genetic algorithm by virtue of the feature parameters of the acoustic emission signals at different rock mass instability stages. According to the technical solution in the present application, the problem in the training process of the BP neural network model that model parameter optimization may be easily trapped in a locally optimal solution is effectively solved.


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