摘要
根据BP神经网络自学习的特点 ,通过训练使模糊变量和隶属函数隐含在网络内部 ,并用模糊逻辑推理模拟驾驶员对车辆进行控制的过程 ,可以使模型更接近于真实的跟驰行为 ,最后用该模型进行了仿真 ,证明其可行性
Car-following model is a basic model in traffic microscopic simulation and car-following behavior is one of the complex tasks of driving. It is hard to describe drivers behavior with precise algorithm because of the fuzzy and indetermination character and the circumstance factors which exist during the driving. In this paper,a car-following model is developed, which integrates the self-learning character of the neural network and uses the fuzzy inference theory to simulate the driver to control the vehicle. The simulation result shows the feasibility of the model.
引文
1 王文清,王武宏,钟永刚,等.基于模糊推理的跟驰安全距离控制算法及实现.交通运输工程学报,2003,3(1):72~75
2 DuTimonChihTing,WolfePhilipM .Implementationoffuzzylogicsystemsandneuralnetworksinindustry,ComputerinIndustry,1997(3):261~272
3 汪培庄.模糊集合论及其应用.上海:上海科学技术出版社,1982.160~204
4 冯德益,楼世博模糊数学方法与应用.北京:地震出版社,1983.116~140
5 郭嗣琮,陈刚信息科学中的软件计算方法沈阳:东北大学出版社,2001.150~263
6 贾洪飞,陈良,王建春.应用五轮仪采集车辆跟驰过程描述参数.山东工程学报,2002,16(3):33~36