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Issue title: Special Section: Applications of intelligent & fuzzy theory in engineering technologies and applied science
Guest editors: Stanley Lima and Álvaro Rocha
Article type: Research Article
Authors: Lu, Zhonglianga; b; c; * | Zhu, Xudongb | Wang, Honglid | Li, Qiangd
Affiliations: [a] School of Safety Science and Engineering, Henan Polytechnic University, Jiaozuo, Henan, China | [b] Key Laboratory of Gas Geology and Gas Control, Jiaozuo, Henan, China | [c] The Collaborative Innovation Center of Coal Safety Production of Henan Province, Jiaozuo, Henan, China | [d] School of Mathematics and Information Science, Henan Polytechnic University, Jiaozuo, China
Correspondence: [*] Corresponding author. Zhongliang Lu, Tel.: +8618903893298; Fax: +860391 3989256; E-mail: zhonglianglu@126.com.
Abstract: In order to intuitively analyze the changing rules and characteristics of coal mine accidents in China, we should find the approximate algebraic fitting relation between the number of gas accidents and time variation and better provide theoretical support for the intelligent prediction and prevention of gas accidents. According to the changing trend of the figure of gas accident, the numerical analysis method of discrete data was used to establish different mathematical models of coal mine gas accidents based on exponential function, power function and polynomial respectively by MATLAB software, and compare the errors of different models. Comparing with the mathematical models, it can be concluded that the law of year and the number of gas accidents in coal mines accord with the results of the fourth-degree polynomial fitting, indicating that the number of gas accidents shows a quantitative law of fitting, the intelligent prediction for the number of gas accident is realized. The results of the model show that in recent years in our country, gas treatment technology and management have achieved remarkable results, and the correlation prediction and analysis have been carried out based on the model results.
Keywords: Gas accident, mathematical model, intelligent prediction, polynomial fitting, error analysis
DOI: 10.3233/JIFS-169616
Journal: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 3, pp. 2649-2655, 2018
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