Dynamic structure adaptive neural fuzzy control for MIMO uncertain nonlinear systems
Information Sciences: an International Journal
Design and analysis of a self-tuning feedback controller for the Internet
Computer Networks: The International Journal of Computer and Telecommunications Networking
A fuzzy neural network with fuzzy impact grades
Neurocomputing
A novel self-tuning feedback controller for active queue management supporting TCP flows
Information Sciences: an International Journal
Multi-agent congestion control for high-speed networks using reinforcement co-learning
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
| Hi-index | 0.00 |
We propose the use of a neural-fuzzy scheme for rate-based feedback congestion control in asynchronous transfer mode (ATM) networks. Available bit rate (ABR) traffic is not guaranteed quality of service (QoS) in the setup connection, and it can dynamically share the available bandwidth. Therefore, congestion can be controlled by regulating the source rate, to a certain degree, according to the current traffic flow. Traditional methods perform congestion control by monitoring the queue length. The source rate is decreased by a fixed rate when the queue length is greater than a prespecified threshold. However, it is difficult to get a suitable rate according to the degree of traffic congestion. We employ a neural-fuzzy mechanism to control the source rate. Through learning, membership values can be generated and cell loss can be predicted from the status of the queue length. Then, an explicit rate is calculated and the source rate is controlled appropriately. Simulation results have shown that our method is effective compared with traditional methods