Research Article
Model Integrating Fuzzy Argument with Neural Network Enhancing the Performance of Active Queue Management
@ARTICLE{10.4108/eai.4-8-2015.150042, author={Nguyen Kim Quoc and Vo Thanh Tu and Nguyen Thuc Hai}, title={Model Integrating Fuzzy Argument with Neural Network Enhancing the Performance of Active Queue Management}, journal={EAI Endorsed Transactions on Context-aware Systems and Applications}, volume={2}, number={4}, publisher={ICST}, journal_a={CASA}, year={2015}, month={8}, keywords={Congestion Control, Active Queue Management, Fuzzy Logic, Neural Network}, doi={10.4108/eai.4-8-2015.150042} }- Nguyen Kim Quoc
Vo Thanh Tu
Nguyen Thuc Hai
Year: 2015
Model Integrating Fuzzy Argument with Neural Network Enhancing the Performance of Active Queue Management
CASA
ICST
DOI: 10.4108/eai.4-8-2015.150042
Abstract
The bottleneck control by active queue management mechanisms at network nodes is essential. In recent years, some researchers have used fuzzy argument to improve the active queue management mechanisms to enhance the network performance. However, the projects using the fuzzy controller depend heavily on professionals and their parameters cannot be updated according to changes in the network, so the effectiveness of this mechanism is not high. Therefore, we propose a model combining the fuzzy controller with neural network (FNN) to overcome the limitations above. Results of the training of the neural networks will find the optimal parameters for the adaptive fuzzy controller well to changes of the network. This improves the operational efficiency of the active queue management mechanisms at network nodes.
Copyright © 2015 N. K. Quoc et al., licensed to ICST. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
