改进Verhulst模型的铁路客运量预测研究  

Railway passenger volume forecast based on improved Verhulst model

作　　者：付洁 黄洪[1] FU Jie;HUANG Hong(College of Information Science and Technology,Southwest Jiaotong University,Chengdu 611756,China)

机构地区：[1]西南交通大学信息科学与技术学院

出　　处：《信息技术》2019年第7期159-161,166,共4页Information Technology

摘　　要：针对传统Verhulst模型在选取背景值缺乏合理性和科学性的不足之处,文中提出一种相对严谨的初始值选取优化模型,在已有的无须设定初始值约束的优化模型下进行进一步改进,并以铁路客运量数据为测试数据,研究两种优化模型的优缺点及适用范围。实验结果表明,文中提出的初始值优化模型在原始数据呈现非线性曲折状态时预测精度相对已有的优化模型更高,但在原始数据曲线相对平滑时,预测精度低于已有的优化模型。In view of the shortcomings of traditional Verhulst model in selecting background values,this paper proposes a relatively rigorous initial value selection optimization model.The existing optimization models without initial value constraints are further improved,and the advantages,disadvantages and application scope of the two optimization models are studied with railway passenger volume data as test data.The experimental results show that the prediction accuracy of the initial value optimization model proposed in this paper is higher than that of the existing optimization model when the original data presents a non-linear tortuous state.However,when the original data curve is relatively smooth,the prediction accuracy is lower than the existing optimization model.

关 键 词：VERHULST模型 初始值优化 铁路客运量 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]