摘要
针对综合管廊造价高于传统市政管线设施,其估算具有影响因素众多、非线性等特点,综合考虑管廊长度、截面面积、舱数以及管线入廊个数等10个特征因素,充分利用遗传算法(GA)与BP神经网络模型的优点,建立了基于GA-BP神经网络的预测模型。通过MATLAB仿真试验,对综合管廊的投资估算进行预测研究,并与传统BP神经网络的计算结果进行对比。相关测试表明:检验样本的模拟输出值与样本真实值呈线性吻合,相对误差基本在5%以内,说明该模型预测综合管廊的投资估算比传统BP神经网络模型具有更高的精度和一定的实际应用价值。
The cost of comprehensive pipe gallery is higher than that of traditional municipal pipeline facilities,and its investment estimation has the characteristics of having many influence factors,nonlinearity and so on.Considering 10 characteristic factors such as the length of the corridor,the cross-sectional area,the number of cabins,and the number of pipelines,a prediction model was established based on GA-BP neural network,combining with the advantages of Genetic Algorithm(GA)and BP neural network model.Through MATLAB simulation experiments,the investment estimation of comprehensive pipe gallery was predicted and compared with calculating results of traditional BP neural network.The results show that the simulation output value of the test sample is in good linear agreement with the true value of the test sample,and the error is less than 5%.Compared with traditional BP neural network model,GA-BP neural network model has a higher computational accuracy,which indicates that GA-BP neural network model has certain feasibility and e ffectiveness in engineering applications.
作者
胡庆国
蔡孟龙
何忠明
HU Qing-guo;CAI Meng-long;HE Zhong-ming(School of Traffic and Transportation Engineering,Changsha University of Science&Technology,Changsha 410114,China)
出处
《长沙理工大学学报（自然科学版）》
CAS
2020年第2期68-74,共7页
Journal of Changsha University of Science and Technology:Natural Science
关键词
GA-BP神经网络
综合管廊
主成分分析
投资估算
预测模型
GA-BP neural network
comprehensive pipe gallery
principal component analysis
investment estimation
prediction model
