  Addressing performance degradations in end-to-end congestion control has been
one of the most active research areas in the last decade. Active queue
management (AQM) aims to improve the overall network throughput, while
providing lower delay and reduce packet loss and improving network. The basic
idea is to actively trigger packet dropping (or marking provided by explicit
congestion notification (ECN)) before buffer overflow. Radial bias function
(RBF)-based AQM controller is proposed in this paper. RBF controller is
suitable as an AQM scheme to control congestion in TCP communication networks
since it is nonlinear. Particle swarm optimization (PSO) algorithm is also
employed to derive RBF parameters such that the integrated-absolute error (IAE)
is minimized. Furthermore, in order to improve the robustness of RBF
controller, an error-integral term is added to RBF equation. The results of the
comparison with Drop Tail, adaptive random early detection (ARED), random
exponential marking (REM), and proportional-integral (PI) controllers are
presented. Integral-RBF has better performance not only in comparison with RBF
but also with ARED, REM and PI controllers in the case of link utilization
while packet loss rate is small.
