基于FOA-BP的电磁辐射法预测煤与瓦斯突出  

Prediction of Coal and Gas Outburst by Electromagnetic Radiation Based on FOA-BP

作　　者：李洋 刘晓悦 

机构地区：[1]华北理工大学电气工程学院,河北唐山063210

出　　处：《华北理工大学学报（自然科学版）》2018年第1期56-63,共8页Journal of North China University of Science and Technology：Natural Science Edition

摘　　要：由于煤与瓦斯突出等动力灾害过程具有很大的非线性与强耦合性,传统的电磁辐射法在预测过程中存在着较大的误差,针对这一情况,提出了基于果蝇算法改进BP神经网络与电磁辐射法相结合的预测模型。对煤与瓦斯突出与电磁辐射的响应模型进行了研究分析,并利用FOA优化BP神经网络提高其收敛速度与预测精度。以开滦煤矿样本为例,通过仿真实验验证,该模型能够准确地预测煤与瓦斯突出灾害,为指导煤矿安全生产提供了依据。Because of the great nonlinearity and strong coupling in the process of coal and gas outburst,and the larger error in the prediction process in traditional electromagnetic radiation method,aprediction model was proposed based on BP neural network improved by fruit fly algorithm and electromagnetic radiation method.Firstly,the response model of coal and gas outburst and electromagnetic radiation was studied and analyzed.Secondly,the BP neural network was optimized by FOA to improve its convergence speed and prediction accuracy.Taking the sample of Kailuan Coal Mine as an example,the coal and gas outburst disaster can be accurately predicted through the simulation experiment,and it provides the basis for guiding coal mine safety production.

关 键 词：果蝇算法 BP神经网络 电磁辐射法 煤与瓦斯突出 

分 类 号：TD771[矿业工程—矿井通风与安全]