Adaptive Neuron Controller with Fuzzy SelfTuning Gain for Queue Management

doi:10.13190/jbupt.201002.99.276

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2010, Vol. 33 ›› Issue (2): 99-104.doi: 10.13190/jbupt.201002.99.276

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Adaptive Neuron Controller with Fuzzy SelfTuning Gain for Queue Management

WANG Hao, TIAN Zuo-hua

(Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China)

Received:2009-06-18 Revised:2009-11-18 Online:2010-04-28 Published:2010-04-28

Abstract

Abstract:

In light of the congestion control system with timevarying parameters and nonlinear property, a neuron control algorithm with fuzzy selftuning gain (FNAQM) 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 endtoend mark probability is calculated by the neuron controller, in which the weights are adjusted online 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 selflearning ability, being simple in form and easy to implement. Simulation in network simulator2(NS2) demonstrates that FNAQM can quickly stabilize the queue length to the target with small jitter, and shows strong robustness against dynamic traffics and nonresponsive flows. 

Key words: congestion control, active queue management, single neuron, fuzzy selftuning

WANG Hao TIAN Zuo-hua. Adaptive Neuron Controller with Fuzzy SelfTuning Gain for Queue Management[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2010, 33(2): 99-104.

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Adaptive Neuron Controller with Fuzzy SelfTuning Gain for Queue Management

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