Study on TCP/AQM network congestion with adaptive neural network and barrier Lyapunov function

被引：15
|
作者：
Wang, Kun [1 ]
Liu, Yang [1 ]
Liu, Xiaoping [2 ]
Jing, Yuanwei [1 ]
Dimirovski, Georgi M. [3 ,4 ]
机构：
[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Liaoning, Peoples R China
[2] Lakehead Univ, Dept Elect Engn, Thunder Bay, ON P7B 5E1, Canada
[3] Dogus Univ, Sch Engn Dept, TR-34722 Istanbul, Turkey
[4] SS Cyril & Methodius Univ, Sch FEIT, Karpos 2, Skopje 1000, Macedonia
来源：
NEUROCOMPUTING | 2019年 / 363卷
关键词：
TCP/AQM Network; Congestion control; Barrier Lyapunov function; Adaptive NN control; ACTIVE QUEUE MANAGEMENT; FEEDBACK NONLINEAR-SYSTEMS; DYNAMIC SURFACE CONTROL; PRESCRIBED PERFORMANCE; TRACKING CONTROL; AQM; DISTURBANCES; CONTROLLER; STABILITY; DESIGN;
D O I：
10.1016/j.neucom.2019.08.024
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A novel network congestion algorithm is introduced for Transmission Control Protocol/Active Queue Management (TCP/AQM) system in this paper. The established TCP/AQM system is more accurate and general. Moreover, an adaptive congestion controller is designed by virtue of the Barrier Lyapunov Function (BLF), backstepping-like and Neural Networks (NNs) approximation techniques, by which the transient and steady-state performances on the tracking error can be pre-given and other signals of the closed-loop system also are verified to be semi-globally, uniformly and ultimately bounded. Finally, a comparison example is considered to demonstrate the feasibility and superiority of the presented scheme. (C) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页码：27 / 34
页数：8
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