基于改进QGA-ELM的瓦斯涌出量预测模型  

Prediction model of gas emission rate based on improved QGA-ELM

作　　者：星宁江 周文铮 刘雨竹 XING Ningjiang;ZHOU Wenzheng;LIU Yuzhu(Lu’an Group Puxian Heilong Coal Industry,Linfen 041000,China;Liaoning Technical University,Huludao 125100,China)

机构地区：[1]潞安集团蒲县黑龙煤业,山西临汾041000 [2]辽宁工程技术大学,辽宁葫芦岛125100

出　　处：《矿业安全与环保》2024年第5期38-45,共8页Mining Safety & Environmental Protection

基　　金：国家自然科学基金项目(51974151)。

摘　　要：针对现有的瓦斯涌出量预测方法普遍未定量分析数据自身因素影响的问题,提出一种改进量子遗传算法(IQGA)优化极限学习机(ELM)瓦斯涌出量预测模型。采用孤立森林(iForest)算法检测绝对瓦斯涌出量的概念漂移,并选择Attention机制的CNN-BiLSTM算法修正概念漂移异常值;利用相关性分析法(PCC)降维处理输入变量,确定预测模型的辅助变量;引入动态调整量子旋转角、量子交叉、量子变异及量子灾变操作获得改进量子遗传算法(IQGA),提升算法寻优能力和泛化能力,使用IQGA对ELM参数寻优。以决定系数(R 2)、平均绝对误差(MAE)、均方根误差(RMSE)及平均绝对百分比误差(MAPE)为指标进行评估,结果表明:IQGA-ELM模型测量误差最小,指标分别为0.985、0.018、0.026及2.56%,预测效果优于其他模型,预测精确度更高。To investigate the problem that existing gas emission prediction methods generally do not quantitatively analyze the influence of the data itself,an improved quantum genetic algorithm(IQGA)optimized extreme learning machine(ELM)gas emission prediction model is developed.The conceptual-drifting of absolute gas emission is detected by iForest algorithm,and the outliers generated by the conceptual-drifting of absolute gas emission are corrected by combining convolutional neural network(CNN),bidirectional long-term short-time memory network(BiLSTM),and attention mechanism.The correlation analysis(PCC)dimensionality reduction method was employed to process the input variables and determine the auxiliary variables of the prediction model.Finally,the quantum genetic algorithm(QGA)is improved by dynamically adjusting the quantum rotation angle,quantum crossing,quantum variation and quantum catastrophe operation.Optimization and generalization ability of the algorithm were therefore enhanced.IQGA was used to optimize ELM parameters.Taking the decision coefficient(R 2),mean absolute error(MAE),root mean square error(RMSE),and mean absolute percentage error(MAPE)as the indexes to carry out the evaluation,The results show that the IQGA-ELM model had the smallest measurement errors of 0.985,0.018,0.026 and 2.56%,respectively.The improved gas emission prediction model exhibit higher prediction accuracy than other models.

关 键 词：瓦斯涌出量 概念漂移 量子遗传 极限学习机 预测方法 

分 类 号：TD712[矿业工程—矿井通风与安全] TH865[机械工程—仪器科学与技术]