Packet Switching Networks Traffic Prediction Based on Radial Basis Function Neural Network

• Autorzy: Zaleski A. , Kacprzak T.
• Języki publikacji: EN

Abstrakty

EN

New multimedia applications require Quality of Service support, which is still not successfully implemented in current packet-switched networks implementations. This paper presents a concept of neural network predictor, suitable for prediction of short-term values of traffic volume generated by end user. The architecture is Radial Basis Function neural network, optimized with respect to a number of neurons. Testing mode of the neural network is very fast, what enables application of this tool in nodes of telecommunication network. This would help to warn a network management system on early symptoms of congestion expected in the near future and avoid the network overload.

Słowa kluczowe

EN

telecommunication traffic; congestion control; neural networks; time series prediction; QoS; traffic flow

PL

kontrola przeciążeń; sieci neuronowe; predykcja szeregów czasowych; QoS; natężenie ruchu

• Wydawca: Wydawnictwo Politechniki Łódzkiej
• Czasopismo: Journal of Applied Computer Science
• Rocznik: 2010
• Tom: Vol. 18, nr 2
• Strony: 91--101
• Opis fizyczny: Bibliogr. 12 poz.

Twórcy

autor

Zaleski A.

autor

Kacprzak T.

• Institute of Electronics, Technical University of Łódź Wólczańska 211/215, 90-924 Łódź, Poland,

Bibliografia

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