| 交通流灰色RBF网络非线性组合预测方法 |
| |
| 引用本文： | 张敬磊,王晓原.交通流灰色RBF网络非线性组合预测方法[J].数学的实践与认识,2011,41(19). |
| |
| 作者姓名： | 张敬磊 王晓原 |
| |
| 作者单位： | 山东理工大学交通与车辆工程学院,山东淄博,255091 |
| |
| 基金项目： | 国家自然科学基金(61074140); 山东省自然科学基金(ZR2010FM007); 山东理工大学青年教师发展支持计划 |
| |
| 摘 要： | 针对智能交通系统的开发,提出一种基于灰色GM(1,1)模型和RBF网络非线性组合的短时交通流预测方法.该方法采用三层结构的RBF网络将两种单一预测方法(灰色GM(1,1)模型和RBF网络)进行了非线性组合.利用实测数据对组合方法进行了仿真实验,结果表明:非线性组合模型的预测准确性高于单独的RBF网络预测的准确性;组合模型发挥了两种单一方法各自的优势,是短时交通流预测的有效方法.
|
| 关 键 词： | 交通流 短时预测 GM(1 1)模型 RBF神经网络 非线性组合预测 |
Non-linear Combined Approach to Traffic Flow Prediction Based on Gray RBF Network |
| |
| ZHANG Jing-lei,WANG Xiao-yuan.Non-linear Combined Approach to Traffic Flow Prediction Based on Gray RBF Network[J].Mathematics in Practice and Theory,2011,41(19). |
| |
| Authors: | ZHANG Jing-lei WANG Xiao-yuan |
| |
| Institution: | ZHANG Jing-lei,WANG Xiao-yuan (School of Transportation and Vehicle Engineering,Shandong University of Technology,Zibo 255091,China) |
| |
| Abstract: | Aimed at developing Intelligent Transportation System,combined RBF network and GM(1,1) forecast,a new method of short-term traffic flow prediction is put forward.The hybrid forecasting approach combined the two methods making use of the non-linear RBF neural network which has a structure of three layers.The simulation test of the forecasting approaches was taken placed used field data.Results show that the non-linear hybrid model, which takes advantage of the unique strength of the two models in linear and ... |
| |
| Keywords: | traffic flow short-term prediction GM(1 1) model RBF neural network nonlinear hybrid prediction |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
