基于粗糙集的高速公路交通流神经网络模型  

Neural Network Model of Freeway Traffic Flow Based on Rough Sets

作　　者：肖健梅[1] 张腾飞[1] 王锡淮[1] 

机构地区：[1]上海海运学院电气自动化系,上海200135

出　　处：《计算机工程》2005年第10期193-196,共4页Computer Engineering

基　　金：国家自然科学基金资助项目(60074004);上海市教育委员会科研基金资助项目(03IK09)

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。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.

关 键 词：粗糙集 RBF神经网络 动态建模 高速公路交通流 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]