A NEURAL-NETWORK APPROACH TO FREEWAY NETWORK TRAFFIC CONTROL

被引：4

作者：

PAPAGEORGIOU, M ^{[1
]}

MESSMER, A ^{[1
]}

AZEMA, J ^{[1
]}

DREWANZ, D ^{[1
]}

机构：

[1] TECH UNIV MUNICH,LEHRST STEUERUNGS & REGELUNGSTECH,MUNICH,GERMANY

来源：

CONTROL ENGINEERING PRACTICE | 1995年 / 3卷 / 12期

关键词：

APPLIED NEURAL CONTROL; BACKPROPAGATION ALGORITHMS; CONJUGATE GRADIENT METHOD; FEEDFORWARD NETWORKS; NEURAL NETWORKS; OPTIMAL CONTROL; ROUTING ALGORITHMS; TRAFFIC CONTROL;

D O I：

10.1016/0967-0661(95)00184-V

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The paper investigates the application of a feedforward neural network approach to freeway network control via variable direction recommendations at bifurcation locations. A nonlinear control problem is formulated and solved first by use of computationally expensive nonlinear optimization techniques. A feedforward neural network is then trained by optimally adjusting its weights so as to reproduce the optimal control law for a limited number of traffic scenarios. Generalisation properties of the neural network are investigated and a discussion of advantages and disadvantages compared with alternative control approaches is provided.

引用

页码：1719 / 1726

页数：8

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