DocumentCode :
1004474
Title :
Dynamic Transmission Rate Allocation in Packet Networks Using Recorrent Neural Networks Trained with Real Time Algorithm
Author :
Vieira, Flávio Henrique Teles ; Lemos, Rodrigo Pinto ; Lee, Luan Ling
Volume :
1
Issue :
1
fYear :
2003
Firstpage :
70
Lastpage :
75
Abstract :
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.
Keywords :
neural Networks; predictive control; traffic; transmission rate; Degradation; Ethernet networks; Intelligent networks; Kalman filters; Monitoring; Neural networks; neural Networks; predictive control; traffic; transmission rate;
fLanguage :
English
Journal_Title :
Latin America Transactions, IEEE (Revista IEEE America Latina)
Publisher :
ieee
ISSN :
1548-0992
Type :
jour
DOI :
10.1109/TLA.2003.1468622
Filename :
1468622
Link To Document :
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