Adaptive neuron controller with fuzzy self-tuning gain for queue management

In light of the congestion control system with time-varying parameters and nonlinear property, a neuron control algorithm with fuzzy self-tuning gain (FN-AQM) is proposed for active queue management. Both queue length and traffic rate are employed as congestion indicators which detect both current and incipient congestion states. Combining the advantages of neuron control and fuzzy control strategies, the end-to-end mark probability is calculated by the neuron controller, in which the weights are adjusted on-line by supervisory Hebb learning rule. Additionally, fuzzy logic control is used to tune the gain of the neuron dynamically for improved network performance. The proposed scheme exhibits good adaptability and self-learning ability, being simple in form and easy to implement. Simulation in network simulator-2(NS2) demonstrates that FN-AQM can quickly stabilize the queue length to the target with small jitter, and shows strong robustness against dynamic traffics and non-responsive flows.