Neuro Fuzzy Model Predictive Control of AQM Networks Supporting TCP Flows
A.R. Maghsoudlou, Roohollah Barzamini, S. Soleimanpour, Javid Jouzdani · 2008
One of the challenges in designing computer networks is "queue management and congestion avoidance". There are several studies for congestion reduction and controlling such as random early detection (RED) and its variants. More recent works on developing congestion avoidance methods include modeling a TCP flow in an active queue management (AQM) of a bottlenecked network link. Rather than classical control theories, that are applied to improve performance and stability of network flows, some studies are developed based on new control tools such as neural networks. In this article a neuro-fuzzy controller for active queue management is proposed. In this model, the number of neurons are determined according to complexity of the model and instead of using random values for initializing networkpsilas weights, near to optional values are used. The proposed method decreases the network training time and ensures convergence with higher probability. The results of this method show superior performance over other previous controllers such as classical and neural networks methods.