Abstract
We use Active Queue Management (AQM) strategy for congestion avoidance in Transmission Control Protocol (TCP) networks to regulate queue size close to a reference level. In this paper we present two efficient and new AQM systems as a queue controller. These methods are designed using Improved Neural Network (INN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). Our aim is low queue variation, low steady state error and fast response with using these methods in different conditions. Performance of the proposed controllers and disturbance rejection is compared with two well-known AQM methods, Adaptive Random Early Detection (ARED), and Proportional-Integral (PI). Our AQM methods are evaluated through simulation experiments using MATLAB.
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Hosseini, S.H., Shabanian, M., Araabi, B.N. (2009). A Neuro-Fuzzy Control for TCP Network Congestion. In: Mehnen, J., Köppen, M., Saad, A., Tiwari, A. (eds) Applications of Soft Computing. Advances in Intelligent and Soft Computing, vol 58. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89619-7_10
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DOI: https://doi.org/10.1007/978-3-540-89619-7_10
Publisher Name: Springer, Berlin, Heidelberg
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