计算机工程与应用 ›› 2007, Vol. 43 ›› Issue (25): 197-199.

• 工程与应用 • 上一篇    下一篇

基于蚁群粒子群的模糊神经网络交通流量预测

于万霞1,2,杜太行1,郑宏兴2   

  1. 1.河北工业大学,天津 300130
    2.天津工程师范学院 电子工程系,天津 300222
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2007-09-01 发布日期:2007-09-01
  • 通讯作者: 于万霞

Fuzzy neural network model for forecasting short-time traffic flow based on ant algorithm and Particle Swarm Optimization

YU Wan-xia1,2,DU Tai-hang1,ZHENG Hong-xing2   

  1. 1.Hebei Univ. of Tech.,Tianjin 300130,China
    2.Department of Electronics Technology,Tianjin Univ. of Technology and Education,Tianjin 300222,China
  • Received:1900-01-01 Revised:1900-01-01 Online:2007-09-01 Published:2007-09-01
  • Contact: YU Wan-xia

摘要: 实时准确的交通流量预测是智能交通诱导和交通控制实现的前提和关键。针对城市交通流的特点,建立了模糊神经网络预测模型,并将全局优化的蚁群算法和粒子群算法组成递阶结构优化模糊神经网络的参数。算法中,主级为蚁群算法,进行全局搜索;从级为粒子群算法,进行局部搜索。仿真结果表明该模型能够取得比梯度下降法更高的预测精度。

关键词: 短时交通流, 预测模型, 模糊神经网络, 粒子群算法, 蚁群算法

Abstract: Real-time and accurate traffic flow prediction is very important to the intelligent traffic guidance and control.According to the characteristics of short-time traffic flow,a fuzzy neural network model has been proposed to solve short-time traffic flow prediction.The paper combines Particle Swarm Optimization(PSO) algorithm with ant algorithm for training the fuzzy neural network.The algorithm is formulated in a form of hierarchical structure.The master level is ant algorithm and slave level is PSO.The global search is performed at the master level,while the local search is carried out at the slave level.The simulation results demonstrate the proposed model can improve prediction accuracy,compared with BP based training techniques.

Key words: short-time traffic flow, prediction model, fuzzy neural network, Particle Swarm Optimization algorithm, ant algorithm