煤矿安全2019，Vol.50Issue(12)：152-157,6.DOI:10.13347/j.cnki.mkaq.2019.12.035
基于长短时记忆神经网络的回采工作面瓦斯浓度动态预测
Dynamic Prediction of Gas Concentration in Mining Face Based on Long Short-term Memory Neural Network
摘要
Abstract
To improve the prediction accuracy of gas concentration in mining face, considering that gas concentration is restricted by historical state, a dynamic prediction model of gas concentration in mining face based on long short-term memory neural network (LSTMNN)is proposed. Using the measured data of gas concentration in a coal mining face in Shanxi Province, the model learning training samples were constructed and the prediction effect was tested. The research shows that LSTMNN algorithm filters the information of gas concentration in the past period through forgetting and memory process, overcomes the short board of traditional prediction method which regards the output value independently, and improves the accuracy and reliability of mine gas concentration prediction; by comparing the prediction results of LSTMNN algorithm with the measured values, the average absolute error, average relative error, root mean square error and Nash model efficiency index of the prediction model were respectively 0.004 319, 0.800 6%, 0.005 714 and 0.436 3.分类
矿业与冶金引用本文复制引用
孙卓越,曹垚林,杨东,韩楚健,马小敏,赵岳然..基于长短时记忆神经网络的回采工作面瓦斯浓度动态预测[J].煤矿安全,2019,50(12):152-157,6.基金项目
国家自然科学基金青年科学基金资助项目(51304119) （51304119）
国家重点研发计划资助项目(2017YFC0804205) （2017YFC0804205）
