作　　者： ; ; ; ;

机构地区： 吉林大学

出　　处： 《吉林大学学报（工学版）》 2011年第4期938-943,共6页

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。 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 sample to enhance the short-term traffic flow prediction accuracy,reduce the prediction time consumption.The model was validated using the practical road network data.The results show that the statistic index MARE of prediction error of KSOM-BP neural network is less than 7%,and less by 4% than that of BP network based on the whole sample without classification.The modeling time consumption of KSOM-BP neural network is also less than BP neural network without sample classification.All of this proves the validation and state-of-the-art of the proposed method.

关 键 词： 交通运输系统工程 交通流短时预测 样本分类拟合 神经网络 动量 自适应学习速率

领　　域： [交通运输工程] [交通运输工程]