基于ANFIS的煤体瓦斯渗透率预测模型研究  

Forecast Model of Coal Gas Permeability Based on ANFIS

作　　者：张炎亮[1] 雷俊辉 毕闰芳 

机构地区：[1]郑州大学管理工程学院,河南郑州450001

出　　处：《煤矿开采》2017年第1期101-104,共4页Coal Mining Technology

基　　金：国家自然科学基金资助项目(71271194);河南省高等学校重点科研项目计划(16A630035);河南省基础与前沿技术研究计划项目(162300410073);教育部人文社会科学研究青年基金资助项目(11YJC630291)

摘　　要：为有效预测煤体瓦斯渗透率,预警井下作业时瓦斯浓度变动,利用神经网络的自适应学习能力和模糊推理系统的经验知识建立自适应神经模糊推理系统(ANFIS)预测模型,并基于实验室数据将其预测结果与BP神经网络模型和支持向量机(SVM)模型的预测值作对比。研究结果表明:ANFIS模型的收敛速度快,预测值与实测值相符度高;在误差精度、训练速度和收敛性等方面,其性能优于其他两种模型,可通过有效应力、瓦斯压力、温度和抗压强度对瓦斯渗透率进行高精度的预测。In order to forecast coal gas permeability effectively, warning gas density variation underground, self adaption neural fuzzy inference system （ANFIS） forecast model was conducted by self adaption studying ability of neural network and experiential knowledge of fuzzy inference system, based on experimental data, the forecast results and the results from BP neural network model and SVM model was compared. The results showed that convergence rate was quickly for ANFIS model, forecast value approached practical value better, it’ s peifomianc.es were better than other two models from error precision, training speed and convergence rate and so on . The high precision forecast of gas permeability could be forecast form effective stress, gas pressure, temperature and compressive strength .

关 键 词：ANFIS 瓦斯渗透率 BP神经网络 SVM 预测模型 

分 类 号：TD712[矿业工程—矿井通风与安全]