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›› 2005, Vol. 31 ›› Issue (10): 202-205.

• 论文 • 上一篇    下一篇

基于粗糙集的高速公路交通流神经网络模型

肖健梅,张腾飞,王锡淮   

  1. 上海海运学院电气自动化系,上海海运学院电气自动化系,上海海运学院电气自动化系 上海 200135
  • 发布日期:2005-05-25

Neural Network Model of Freeway Traffic Flow Based on Rough Sets

XIAO Jianmei, ZHANG Tengfei, WANG Xihuai   

  1. (Department of Electrical and Automation, Shanghai Maritime University, Shanghai 200135)
  • Published:2005-05-25

摘要: 利用粗糙集能够处理模糊、不确定知识和神经网络对非线性函数具有任意逼近能力的优点,提出一种粗糙集和RBF网络集成的动态系统建模算法。并利用这种方法建立具有复杂动态特性和不确定性的高速公路宏观交通流动态模型,仿真结果表明了该方法的有效性。

关键词: 粗糙集, RBF神经网络, 动态建模, 高速公路交通流

Abstract: Rough set is a powerful mathematics tool, which can deal with fuzzy and uncertain knowledge, and RBF neural network has the ability to approach any nonlinear function precisely. A dynamic modeling method is presented using the rough sets and RBF network integrated technology for complex system. The method is applied to model the freeway traffic flow system with complex dynamic characteristics and uncertainties. The simulation results prove the validity of this method.

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