| 基于核自组织映射-前馈神经网络的交通流短时预测 |
| |
| 引用本文： | 龚勃文,林赐云,李静,杨兆升. 基于核自组织映射-前馈神经网络的交通流短时预测[J]. 吉林大学学报(工学版), 2011, 0(4): 938-943 |
| |
| 作者姓名： | 龚勃文 林赐云 李静 杨兆升 |
| |
| 作者单位： | 吉林大学汽车仿真与控制国家重点实验室;吉林大学交通学院;吉林大学汽车工程学院; |
| |
| 基金项目： | “863”国家高技术研究发展计划项目(2009AA11Z218,2009AA11Z208); 中国博士后科学基金项目(20100481054) |
| |
| 摘 要： | 提出了一种基于KSOM-BP神经网络的交通流短时预测模型。利用基于核函数的样本自组织映射神经网络(KSOM),在没有任何先验知识的情况下,自组织、自学习地将具有相似统计特性的历史样本划分成一类,促使分类样本统计特性更集中显著。对每个类别的样本分别建立动量-自适应学习速率的BP神经网络预测模型,以期提高交通流短时预测精度,减少预测时间。结合实际城市道路数据对模型进行验证。验证结果表明:KSOM-BP神经网络的预测误差统计指标MARE小于7%,比基于全部样本训练的BP神经网络的MARE减少4%左右;同时,KSOM-BP神经网络建模时间更短,证明了本文方法的有效性和先进性。
|
| 关 键 词： | 交通运输系统工程 交通流短时预测 样本分类拟合 KSOM-BP神经网络 动量-自适应学习速率 |
Short-term traffic flow prediction based on KSOM-BP neural network |
| |
| GONG Bo-wen,,LIN Ci-yun,LI Jing,YANG Zhao-sheng. Short-term traffic flow prediction based on KSOM-BP neural network[J]. Journal of Jilin University:Eng and Technol Ed, 2011, 0(4): 938-943 |
| |
| Authors: | GONG Bo-wen LIN Ci-yun LI Jing YANG Zhao-sheng |
| |
| Affiliation: | GONG Bo-wen1,2,LIN Ci-yun1,LI Jing3,YANG Zhao-sheng1,2(1.State Key Laboratory of Automotive Simulation and Control,Jilin University,Changchun 130022,China,2.College of Transportation,3.College of Automotive Engineering,China) |
| |
| Abstract: | A short-term traffic flow prediction model was built based on KSOM-BP neural network.Using the kernel sample self-organizing map(KSOM) neural network,under the condition without a priori knowledge,the history samples with similar statistic character were classified into several categories by self-organizing and self-learning to enhance the statistic significance of the classified sample.A back-propagation(BP) neural network prediction model was built for the momentum-adaptive learning rate of every category... |
| |
| Keywords: | engineering of communications and transportation system traffic flow short-term prediction sample classification fitting KSOM-BP neural network momentum coefficient and adaptive learning rate |
| 本文献已被 CNKI 等数据库收录！ |
|
