DocumentCode
2885858
Title
Research on Network Node Closed Loop Control Mechanism Based on Probability Drop Scheme
Author
Sun, De-Hui ; Zhang, Da
Author_Institution
Key Lab. of Beijing for Field-bus Technol. & Autom., North China Univ. of Technol., Beijing
fYear
2006
fDate
13-16 Aug. 2006
Firstpage
252
Lastpage
255
Abstract
In order to solve the increasingly serious network congestion, a node closed loop PID control mechanism is proposed, which is based on BP neural network (abbr. BPNN). This mechanism works with active queue management (abbr. AQM) scheme with probability drop strategy, which forms a closed loop by controlling node buffer average queue size. The method tries to avoid the disadvantages of the traditional solutions, which control input and output traffic flow. This paper presents the closed loop configuration figure, and gives the algorithm realizations of PID controller model based on BP neural network and probability drop strategy. Simulation that runs on the OPNET software is carried out on the node model. The simulation results show that the proposed mechanism works well in terms of convergence speed of PID parameters, stability and anti-jamming of the average queue size and the system robustness
Keywords
asynchronous transfer mode; backpropagation; closed loop systems; neurocontrollers; probability; queueing theory; telecommunication computing; telecommunication congestion control; telecommunication traffic; three-term control; OPNET software; active queue management; backpropagation neural network; network congestion; network node closed loop PID control mechanism; node buffer average queue size control; probability drop scheme; traffic flow control; Automatic control; Communication system control; Communication system traffic control; Cybernetics; Laboratories; Machine learning; Neural networks; Robust control; Robust stability; Size control; Sun; Three-term control; Traffic control; Active queue management; Closed loop control; Neural network; Probability drop;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location
Dalian, China
Print_ISBN
1-4244-0061-9
Type
conf
DOI
10.1109/ICMLC.2006.258963
Filename
4028068
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