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Congestion Control is concerned with allocating the network resources such that the network can operate at an optimum performance level when the demand exceeds or it is near the capacity of the network resources. This paper presents a novel scheme of adaptive Neuro-Fuzzy Inference Controller (ANFIS). The advantages of both Fuzzy Logic and Neural Networks are combined together to design the ANFIS....
Network congestion control, due to increasing number of Internet services with various quality of service (QoS) demands, has become the focus of current research. In this paper, a novel scheme of adaptive Smith Predictor (SP) controller using Neural Network for active queue management (NNAQM) is presented. Smith predictor is used to overcome the disadvantages such as influence of time delay, in particular,...
This paper presents an investigation into a robust controller for active queue management (AQM) using Smith predictor (SP). A new SP random early detector (RED) controller is proposed and compared with traditional RED based controller. It is worth noting that the RED controller is augmented by a Smith predictor structure for plants with medium and high time delays. The proposed Smith predictor based...
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