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Prediction Model for Gas Outburst Intensity of Coal Mining Face Based on Improved PSO and LSSVM

Haibo Liu1,*, Yujie Dong2, Fuzhong Wang1
1 School of Electric Engineering and Automation, Henan Polytechnic University, Jiaozuo, 454000, China
2 College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454000, China
* Corresponding Author: Haibo Liu. Email:

Energy Engineering 2021, 118(3), 679-689. https://doi.org/ 10.32604/EE.2021.014630

Received 13 October 2020; Accepted 30 November 2020; Issue published 22 March 2021

Abstract

For the problems of nonlinearity, uncertainty and low prediction accuracy in the gas outburst prediction of coal mining face, the least squares support vector machine (LSSVM) is proposed to establish the prediction model. Firstly, considering the inertia coefficients as global parameters lacks the ability to improve the solution for the traditional particle swarm optimization (PSO), an improved PSO (IPSO) algorithm is introduced to adjust different inertia weights in updating the particle swarm and solve the fitness to stagnate. Secondly, the penalty factor and kernel function parameter of LSSVM are searched automatically, and the regression accuracy and generalization performance is enhanced by applying IPSO. Finally, to verify the proposed prediction model, the model is applied for gas outburst prediction of Jiuli Hill coal mine in Jiaozuo City, and the results are compared with that of PSO-SVM model, IGA-LSSVM model and BP model. The results show that the relative errors of the proposed model are not greater than 2.7%, and the prediction accuracy is higher than other three prediction models. The IPSO-LSSVM model can be used to predict the intensity of gas outburst of coal mining face effectively.

Keywords

Mining face; gas outburst; least squares support vector machine; improved particle swarm optimization; prediction

Cite This Article

Liu, H., Dong, Y., Wang, F. (2021). Prediction Model for Gas Outburst Intensity of Coal Mining Face Based on Improved PSO and LSSVM. Energy Engineering, 118(3), 679–689.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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