摘要
大多数的混沌管制算法被用来管制交错点的交通信息流,而目前管制器输入/输出、条例表创建以及MATLAB混沌条例求解方案是被重点强调的。利用MATLAB构建车辆的到达模型和混沌控制仿真模型,并进行测试,测试数据用于减少车辆的平均拖延时间和队列长度。鉴于交通量预料不能满足智能交通约束的需要,文中判辨了交通量数据的内在混沌特点。它包含时间耽搁,嵌入维数,相关维数和Lyapunov指数的准备。人体神经网络模型用于测试。最终,给出了北京环城路上车道交通量测试的一个例子,说明基于混沌时辰序列判辨的神经网络交通量测试在评估数据动态特性和过失管制方面具有很大优势。
Most of the chaotic control algorithms are used to control the traffic information flow at interlaced points,and the input/output of the controller,the creation of the regulation table and the solution of the MATLAB chaos regulation are emphasized.The vehicle arrival model and chaotic control simulation model are constructed and tested with MATLAB.The test data is used to reduce the average delay time and queue length of the vehicle.In view of the fact that traffic volume was not expected to meet the needs of intelligent transportation constraints,the inherent chaos characteristics of traffic volume data were identified.It included time delay,embedding dimension,correlation dimension and Lyapunov index preparation.The human neural network model was used for testing.Finally,an example of the traffic volume test of Beijing ring road was given.It showed that the neural network traffic test based on the identification of chaotic time sequence had a great advantage in evaluating the dynamic characteristics of the data and the negligence control.
作者
屈展
高新芝
赵鑫
QU Zhan;GAO Xin-zhi;ZHAO Xin(Gansu Radio ＆ TV University,Lanzhou 730000,China)
出处
《通信电源技术》
2018年第7期221-223,共3页
Telecom Power Technology
基金
甘肃广播电视大学青年课题(项目编号:2015-QN-04)
关键词
混沌
神经网络
交通量
测试
chaos
neural network
traffic volume
testing
