Title :
An analytical design of GAPIDNN algorithm for AQM
Author_Institution :
Sch. of Manage., Nanjing Univ. of Posts & Telecommun., Nanjing, China
Abstract :
A PID Neural Network algotithm with genetic algorithm, called GAPIDNN, is designed and applied in active queue management (AQM). The genetic algorithm is used to turn the PID Neural Network weight. NS simulation results show that the GAPIDNN algorithm has better control performance than PIDNN. GAPIDNN algorithm shows higher robustness and link utilization under changing network enviroment and large delay.
Keywords :
control system synthesis; delays; genetic algorithms; neurocontrollers; queueing theory; stability; telecommunication congestion control; telecommunication network management; three-term control; AQM; GAPIDNN algorithm design; PID neural network algorithm; PID neural network weight; PIDNN controller; active queue management; control performance; genetic algorithm; network congestion control; network delay; robustness; Algorithm design and analysis; Biological neural networks; Educational institutions; Genetic algorithms; Neurons; Robustness; AQM; PID Neural Network; genetic algorithm; network congestion control;
Conference_Titel :
Mechatronic Sciences, Electric Engineering and Computer (MEC), Proceedings 2013 International Conference on
Conference_Location :
Shengyang
Print_ISBN :
978-1-4799-2564-3
DOI :
10.1109/MEC.2013.6885053
