Citation

Fu H, Zhao J, Liu H, Liu Y, Lu W. China Saf. Sci. J. 2022; 32(10): 40-47.

Copyright

(Copyright © 2022, China Occupational Safety and Health Association, Publisher Gai Xue bao)

DOI

10.16265/j.cnki.issn1003-3033.2022.10.1868

PMID

unavailable

Abstract

In order to address identification problems of signal characteristics of fracture in gas bearing coal during fracture process, with Bi⁃directional Long Short⁃Term Memory network (BiLSTM) as base classifier, a dual strategy coupling optimization identification model for such signal characteristics was proposed based on AdaBoost algorithm and HHO algorithm. Firstly, in view of the problem that proportion of error samples in AdaBoost algorithm increased with iteration, which had an impact on results of final strong classifiers, weight parameters were introduced and weights of weak classifiers were changed so as to improve identification. Then, to determine optimal model parameters, identification parameters and weight parameters were optimized based on HHO, and during optimization process, HHO and improved AdaBoost algorithm produced a coupling effect, which made the identification model's accuracy and stability reach optimal level, resulting in an average accuracy of 91. 36% and standard deviation being reduced to 0.017 4. The results show that the dual strategy coupling optimization model of HHO⁃AdaBoost⁃BiLSTM identification of signal characteristics of fracture in gas bearing coal has higher accuracy and stability. © 2022 China Safety Science Journal. All rights reserved.

Language: zh