传感技术学报2016，Vol.29Issue(7)：1115-1120,6.DOI:10.3969/j.issn.1004-1699.2016.07.027
基于IsoMap和MBFO-SVR的瓦斯涌出量动态预测研究
Based on the IsoMap with MBFO-SVR Gas Emission Dynamic Prediction Research
摘要
Abstract
In order to realize the dynamic prediction of absolute gas emission with high precision and real time in coal mine,this paper puts forward a forecasting method by combining the Isometric feature Mapping(IsoMap)and Support Vector Regression machine(SVR)optimized byModified Bacteria Foraging algorithm(MBFO). Gas emis⁃sion is an emergent property resulting from various interactions,and these factors are complex nonlinear relation⁃ship. Therefore,using the IsoMap,a manifold learning method,is to reduce the dimension of feature extraction in this article. This methodis advantageous to excavate the high dimensioneigenvectorinner relationship by using geo⁃desic distanceto replace the Euclidean distanceand superior to the traditional principal component analysis(PCA);By using MBFO to optimizing parameters of SVR,results analysised by IsoMap are the input of prediction model. Simulation shows that compared with PSO algorithm,the proposed prediction method forecasting accuracy is higher, more conducive to the quantity of gas emission prediction.关键词
瓦斯涌出量/等容特征映射/细菌觅食优化算法/支持向量回归机Key words
gas emission/Isometric feature mapping/bacteria foraging optimization/support vector regression ma-chine分类
计算机与自动化引用本文复制引用
谢国民,单敏柱,付华..基于IsoMap和MBFO-SVR的瓦斯涌出量动态预测研究[J].传感技术学报,2016,29(7):1115-1120,6.基金项目
国家自然科学基金项目（51274118）；辽宁省教育厅基金项目 （）
