Traffic prediction and network resources estimation of VBR MPEG2 sources using adaptively trained neural networks

Doulamis, A; Doulamis, N; Kollias, S

dc.contributor.author	Doulamis, A	en
dc.contributor.author	Doulamis, N	en
dc.contributor.author	Kollias, S	en
dc.date.accessioned	2014-03-01T02:49:01Z
dc.date.available	2014-03-01T02:49:01Z
dc.date.issued	2000	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/34224
dc.subject	Adaptive Algorithm	en
dc.subject	Indexing Terms	en
dc.subject	Non-linear Model	en
dc.subject	Nonlinear Model	en
dc.subject	Traffic Model	en
dc.subject	Traffic Prediction	en
dc.subject	Feedforward Neural Network	en
dc.subject	Neural Network	en
dc.title	Traffic prediction and network resources estimation of VBR MPEG2 sources using adaptively trained neural networks	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/MELCON.2000.880034	en
heal.identifier.secondary	http://dx.doi.org/10.1109/MELCON.2000.880034	en
heal.publicationDate	2000	en
heal.abstract	In this paper, a unified non-linear modeling is proposed appropriate both for on-line traffic prediction and network resources estimation in the case of VBR MPEG-2 coded video sources. A feedforward neural network architecture with tapped delay inputs is adopted to implement the nonlinear model structure. For on-line traffic modeling, a weight adaptation algorithm is activated, to modify the model parameters,	en
heal.journalName	MELECON - IEEE Mediterranean Electrotechnical Conference	en
dc.identifier.doi	10.1109/MELCON.2000.880034	en