引用本文:傅惠,许伦辉,胡刚,王勇.基于Sugeno型神经模糊系统的交通流状态预测算法[J].控制理论与应用,2010,27(12):1637~1640.[点击复制]
FU Hui,XU Lun-hui,HU Gang,WANG Yong.Traffic flow state-forecasting algorithm based on Sugeno neural fuzzy system[J].Control Theory and Technology,2010,27(12):1637~1640.[点击复制]
基于Sugeno型神经模糊系统的交通流状态预测算法
Traffic flow state-forecasting algorithm based on Sugeno neural fuzzy system
摘要点击 1941  全文点击 1372  投稿时间：2009-10-05  修订日期：2010-07-15
查看全文  查看/发表评论  下载PDF阅读器
DOI编号  
  2010,27(12):1637-1640
中文关键词  神经模糊系统  交通流状态预测  动态交通管理
英文关键词  neural fuzzy system  traffic flow states forecasting  dynamic traffic management
基金项目  广东省科技计划资助项目(2009B010800052, 2009B090300388); 广东省教育厅“育苗工程”资助项目(LYM08053).
作者单位E-mail
傅惠* 广东工业大学 机电工程学院 fuhu@gdut.edu.cn 
许伦辉 华南理工大学 土木与交通学院  
胡刚 广东工业大学 机电工程学院  
王勇 广东工业大学 计算机学院  
中文摘要
      从交通流状态的模糊特性出发, 设计基于Sugeno型神经模糊系统的交通流状态预测算法. 选择交通流状态的影响指标作为模糊推理系统的输入、交通流状态作为输出; 据经验对输入、输出划分模糊子集, 给出相应的隶属度函数并制定模糊规则; 建立具有5层结构的神经模糊推理系统, 利用神经网络优化调整模糊推理系统的隶属度函数和模糊规则. 仿真实验表明, 神经网络可直接优化模糊推理系统的隶属度函数, 通过对连接权值的训练间接优化模糊规则, 故Sugeno型神经模糊系统相比常规模糊系统具有更好的交通流状态预测性能.
英文摘要
      According to the fuzziness of traffic flow states, a traffic flow state-forecasting algorithm based on Sugeno neural fuzzy system(NFS) is proposed. In this algorithm, a number of traffic parameters are chosen as inputs, and the traffic flow states are taken as output of a NFS. The fuzzy subsets of inputs and output are given empirically. In addition, the corresponding membership functions and fuzzy IF-THEN rules are also built up by experience. A 5-layer NFS is presented in the given algorithm; and a neural network is used to optimize the fuzzy inference system(FIS). The experiment shows that neural network can optimize the membership functions directly and the fuzzy rules indirectly. Hence, the Sugeno NFS is more effective than the normal FIS in traffic flow state-forecasting.