Citation

Xiao P, Xie X, Shuang H, Liu C, Wang H, Xu J. China Saf. Sci. J. 2020; 30(5): 39-47.

Copyright

(Copyright © 2020, China Occupational Safety and Health Association, Publisher Gai Xue bao)

DOI

10.16265/j.cnki.issn1003-3033.2020.05.007

PMID

unavailable

Abstract

In order to accurately predict gas emission quantity, considering the nonlinearity, time-varying characteristic and complexity of absolute gas emission, KPCA was proposed to conduct dimensionality reduction for influencing factors. Secondly, targeting at problems of BPNNs ' slow convergence and tendency to fall into local optimal solution, CMGA was adopted to optimize BPNN. Then, a coupling algorithm CMGANN based on CMGA and BPNN was constructed to calculate and analyze sample sets formed by historical data of a low gas mine, and KPCA-CMGANN prediction model was established which together with three other network models were used to predict coal mine field data. The results show that KPCA-CMGANN model achieves convergence in 379 time steps, and relative errors of gas emission prediction for four working faces are 0. 58%, 0. 63%, 0. 57% and 0. 45% with an average relative error at only 0. 56%. Its prediction accuracy and convergence speed are superior to comparative model, making it ready to predict gas emission amount accurately and quickly. © 2020 China Safety Science Journal. All rights reserved.

Language: zh