物探与化探2016，Vol.40Issue(2)：327-332,6.DOI:10.11720/wtyht.2016.2.15
基于细菌觅食优化广义回归神经网络的煤层气含量预测
The prediction of the coalbed methane content based on bacteria foraging optimizing generalized regression neural network
摘要
Abstract
Coalbed methane is an important part of the natural gas energy, and determination of coal seam gas content is the key to the study of exploration and development of coal seam. In order to improve the capability of coal bed gas content prediction, this paper puts forward a kind of bacteria foraging optimization algorithm and generalized regression neural network ( BFA⁃GRNN) of the coalbed gas content prediction algorithm. Well logging data and core data of coal seam are used by neural network to establish regression model, bacterial foraging algorithm is used to optimize the model parameters, and artificial factor influences on determining the network struc⁃ture and the process of spreading factor are reduced. According to this algorithm and on the basis of clustering analysis and gray correla⁃tion analysis, seven main factors of coal bed gas content are chosen, which include density, resistivity, ash content etc. BFA⁃GRNN model is set ip by using the data of coal seam, and through the example analysis, the feasibility of this method is verified. The results show that the BFA⁃GRNN model is a true reflection of the nonlinear relationship between the coal seam gas content and the main control factors, and the relative error between predicted values and the measured values is less than 6%, suggesting that using the model to predict coal bed gas content has a good application prospect.关键词
煤层气含量/聚类分析/灰色关联分析/细菌觅食算法/广义回归神经网络/BP神经网络Key words
coalbed methane content/clustering analysis/gray correlation analysis/bacterial foraging algorithm/generalized regres-sion neural network/BP neural network分类
天文与地球科学引用本文复制引用
张瑞,陈刚,潘保芝,蒋必辞,杨雪,刘丹..基于细菌觅食优化广义回归神经网络的煤层气含量预测[J].物探与化探,2016,40(2):327-332,6.基金项目
“十二五”重大专项课题“煤与煤层气地质条件精细探测技术与装备” （）
