计算机科学与探索Issue(1)：121-126,6.DOI:10.3778/j.issn.1673-9418.1309008
基于改进T-S模糊神经网络的交通流量预测
Traffic Flow Prediction Based on Improved T-S Fuzzy Neural Network
摘要
Abstract
Based on the glowworm swarm optimization (GSO) and T-S fuzzy neural network (TSFNN), this paper proposes a prediction algorithm for traffic flow of T-S fuzzy neural network optimized glowworm swarm optimiza-tion (GSOTSFNN). The proposed algorithm uses GSO to get the optimal parameter configuration, thus can perform mapping ability of T-S fuzzy neural network for generalization. The efficiency of the proposed prediction algorithm is tested by the simulation of real traffic flow. The simulation results show that the proposed algorithm has higher forecasting accuracy compared with the traditional T-S fuzzy neural network and T-S fuzzy neural network opti-mized genetic algorithm, so it is feasible and effective in the practical prediction of traffic flow.关键词
智能交通系统(ITS)/萤火虫优化算法(GSO)/T-S模型/模糊神经网络/交通流量/预测Key words
intelligent transportation system (ITS)/glowworm swarm optimization (GSO)/T-S model/fuzzy neural network/traffic flow/prediction分类
信息技术与安全科学引用本文复制引用
侯越..基于改进T-S模糊神经网络的交通流量预测[J].计算机科学与探索,2014,(1):121-126,6.基金项目
The Young Scientific Research Fund Project of Lanzhou Jiaotong University under Grant No.2013006(兰州交通大学青年科学基金项目) （兰州交通大学青年科学基金项目）
