|
|||||
|
|
| 基于Sugeno型神经模糊系统的交通流状态预测算法 | |
| 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. |
| 作者姓名： | 傅惠 许伦辉 胡刚 王勇 |
| 作者单位： | 1. 广东工业大学机电工程学院,广东,广州,510006 2. 华南理工大学土木与交通学院,广东,广州,510640 3. 广东工业大学计算机学院,广东,广州,510006 |
| 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). |
| 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. |
| 关 键 词： | 神经模糊系统 交通流状态预测 动态交通管理 |
| 收稿时间： | 2009/10/5 0:00:00 |
| 修稿时间： | 2010/7/15 0:00:00 |
Traffic flow state-forecasting algorithm based on Sugeno neural fuzzy system |
|
| FU Hui,XU Lun-hui,HU Gang and WANG Yong.Traffic flow state-forecasting algorithm based on Sugeno neural fuzzy system[J].Control Theory & Applications,2010,27(12):1637-1640. | |
| Authors: | FU Hui XU Lun-hui HU Gang and WANG Yong |
| Affiliation: | Faculty of Electromechanical Engineering, Guangdong University of Technology,School of Civil Engineering and Transportation, South China University of Technology,Faculty of Electromechanical Engineering, Guangdong University of Technology,Faculty of Computer, Guangdong University of Technology |
| Abstract: | 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. |
| Keywords: | neural fuzzy system traffic flow states forecasting dynamic traffic management |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |
| 点击此处可从《控制理论与应用》浏览原始摘要信息 | |
| 点击此处可从《控制理论与应用》下载全文 | |