%0 Journal Article
%T Iterative Learning Identification Method for the Macroscopic Traffic Flow Model<br>宏观交通流模型参数的迭代学习辨识方法
%A HOU Zhong-Sheng
%A JIN Shang-Tai
%A ZHAO Ming
%A <br>侯忠生
%A 金尚泰
%A 赵明
%J 自动化学报
%D 2008
%I 
%X By transforming the macroscopic traffic flow model into a more general discrete-time nonlinear system model,an iterative learning identification method is developed to estimate the parameters of the more general discrete- time nonlinear system,so the macroscopic traffic flow parameters as well,based on the repeatability of the macroscopic traffic flow behavior in a freeway.With rigorous analysis,it is shown that the proposed learning identification scheme can guarantee the convergence and robustness.A number of simulation results are provided to demonstrate the efficacy of the proposed approach.
%K Macroscopic traffic flow model
%K iterative learning
%K parameter identification
%K repeatability<br>宏观交通流模型
%K 迭代学习控制
%K 参数辨识
%K 重复性
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=1A4CA08D63B1A07E6CE52D5CBA49628E&yid=67289AFF6305E306&vid=339D79302DF62549&iid=CA4FD0336C81A37A&sid=0401E2DB1F51F8DE&eid=4F2F18DD6F870C2C&journal_id=0254-4156&journal_name=自动化学报&referenced_num=0&reference_num=19