Dynamic Transmission Rate Allocation in Packet Networks Using Recorrent Neural Networks Trained with Real Time Algorithm

In this paper recurrent neural networks are considered to realize traffic prediction in computer network. The transmission rate that must be allocated in order to prevent byte losses and to get an efficient network use is estimated in real time. For such, recurrent neural networks were trained with...

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Bibliographic Details
Published in:Revista IEEE América Latina 2003-10, Vol.1 (1), p.70-75
Main Authors: Vieira, F.H.T., Lemos, R.P., Luan Ling Lee
Format: Article
Language:English
Subjects:
Degradation
Ethernet networks
Intelligent networks
Kalman filters
Monitoring
Neural networks
predictive control
traffic
transmission rate
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Summary:In this paper recurrent neural networks are considered to realize traffic prediction in computer network. The transmission rate that must be allocated in order to prevent byte losses and to get an efficient network use is estimated in real time. For such, recurrent neural networks were trained with real time learning algorithms: RTRL (Real Time Recurrent Learning) and extended Kalman filter. The algorithms are applied in the dynamic transmission rate allocation in a network link, verifying its efficiencies in the traffic prediction and control.
ISSN:1548-0992
1548-0992
DOI:10.1109/TLA.2003.1468622