辽宁工程技术大学学报（自然科学版）2017，Vol.36Issue(6)：593-597,5.DOI:10.11956/j.issn.1008-0562.2017.06.006
BP神经网络模型在煤尘润湿性研究中的应用
Application of BP neural network model to study the coal dust wettability
摘要
Abstract
This paper built a three layers BP neural network to solve the problem of the complicated measurement of wetting contact angle on the solid surface and unreasonable grade division of coal dust wettability.Whose input parameters included thirteen factors,such as chemical composition and characteristic parameter of coal dust,and driving function adopted hyperbolic tangent sigrnoid transfer function.The results show that when the nodes number of hidden layer is 10,the relative errors are at the range from 0.19 ％ to 13.99 ％,and the average relative error is as low as 5.18 ％.The correlation coefficent between measured values and estimate values of coal dust wetting contact angle is 0.933,and the accuracy of wettability classification is 91.67 ％.Thus,the coal dust wetting contact angle and wettability classification results with BP neural network can be used to guide the selection of effective dust-reducing measures for coal mine well.关键词
煤尘/接触角/润湿性分级/BP神经网络/双曲正切S形函数Key words
coal dust/contact angle/wettability classification/BP neural network/hyperbolic tangent sigmoid transfer function分类
矿业与冶金引用本文复制引用
罗根华,马云东,鞠中兰,张博..BP神经网络模型在煤尘润湿性研究中的应用[J].辽宁工程技术大学学报（自然科学版）,2017,36(6):593-597,5.基金项目
国家自然科学基金(U1261121) （U1261121）
