Study on TCP/AQM network congestion with adaptive neural network and barrier Lyapunov function
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CitationWang, K., Liu, Y., Liu, X., Jing, Y., Dimirovski, G. M. (2019). Study on TCP/AQM network congestion with adaptive neural network and barrier Lyapunov function. Neurocomputing, 363, 27-34. https://doi.org/10.1016/j.neucom.2019.08.024.
A novel network congestion algorithm is introduced for Transmission Control Protocol/Active Queue Management (TCP/AQM) system in this paper. The established TCP/AQM system is more accurate and general. Moreover, an adaptive congestion controller is designed by virtue of the Barrier Lyapunov Function (BLF), backstepping-like and Neural Networks (NNs) approximation techniques, by which the transient and steady-state performances on the tracking error can be pre-given and other signals of the closed-loop system also are verified to be semi-globally, uniformly and ultimately bounded. Finally, a comparison example is considered to demonstrate the feasibility and superiority of the presented scheme.
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