Title: Gas outburst prediction based on the intelligent D-S evidence theory

Authors: Caixia Gao; Fuzhong Wang; Zhan Zhang

Addresses: School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000, China ' School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000, China ' School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000, China

Abstract: In this paper, the predicted model of gas outburst is built by combining fuzzy neural network and D-S evidence theory, the overall structure design of gas outburst predicted model is presented, the selection of gas outburst evaluation indicators, the design of fuzzy neural network unit and the design of D-S evidence theory unit are introduced. The eight key factors are selected as the evaluation indicators of gas outburst, and the preliminary judgment of gas outburst state in local point, is made by fuzzy neural network, and then global judgment of gas outburst state in mining working face is made based on D-S evidence theory. The simulated result shows that this method can make accurate judgments of gas outburst state grade, and regarding the judgments of the three kinds of gas outburst state, the accuracy error is less than 0.0048% and the uncertainty value approximates to 0.

Keywords: gas outburst prediction; fuzzy neural network; D-S evidence theory; algorithm design.

DOI: 10.1504/IJCAT.2019.098028

International Journal of Computer Applications in Technology, 2019 Vol.59 No.2, pp.123 - 129

Available online: 22 Feb 2019 *

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