@article{ref1,
title="Prediction of gas emission quantity based on KPCA-CMGANN algorithm",
journal="China safety science journal (CSSJ)",
year="2020",
author="Xiao, P. and Xie, X. and Shuang, H. and Liu, C. and Wang, H. and Xu, J.",
volume="30",
number="5",
pages="39-47",
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.<p /><p>Language: zh</p>",
language="zh",
issn="1003-3033",
doi="10.16265/j.cnki.issn1003-3033.2020.05.007",
url="http://dx.doi.org/10.16265/j.cnki.issn1003-3033.2020.05.007"
}