基于GA-BPNN的巷道围岩变形模量预测  

Prediction of Surrounding Rock Deformation Modulus of Roadway Base on GA-BPNN

作　　者：王德永[1,2] 袁艳斌[1] 陈颖[1] 

机构地区：[1]武汉理工大学资源与环境工程学院 [2]平顶山工业职业技术学院计算机系

出　　处：《金属矿山》2013年第3期9-13,共5页Metal Mine

基　　金：国家高技术研究发展计划(863计划)项目(编号:2009AA12201);平顶山市科技创新人才计划项目(编号:2012061)

摘　　要：研究了遗传算法(GA)在设计和优化BPNN结构时的效能和它在预测岩体变形模量的应用,利用GA找到隐藏层神经元的最优数量以及隐含与输出层的学习因子和动量因子,然后和试错过程进行比较。采用了来源于实际巷道测量的76组数据集验证该方法。利用MSE,MAE,R等性能标准,证明GA-BPNN模型在岩体变形模量预测方面优于BPNN试错模型。The effectiveness of the genetic algorithm （GA） in the design and BPNN structure optimization and its ap- plication in the prediction of rock mass deformation modulus was researched. GA is used to find out the optimal number of neurons in the hidden layer and learning factor and momentum factor of the hidden layer and output layer, and then com- pared with the trial-and-error process. 76 groups of data sets derived from the actual roadway were used to validate this method. With performance criteria such as MSE, MAE and R,it is proved that GA-BPNN model is better than BPNN trial- and-error model in the prediction of rock mass deformation modulus.

关 键 词：巷道围岩 变形模量 预测 GA—BPNN 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]