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Prediction of Net Bandwidth using Artificial neural Network

IJCA Proceedings on National Conference on Innovative Paradigms in Engineering & Technology 2013
© 2013 by IJCA Journal
NCIPET2013 - Number 8
Year of Publication: 2013
Tanushree. Selokar
Sanjay L. Badjate

Tanushree.selokar and Sanjay L Badjate. Article: Prediction of Net Bandwidth using Artificial neural Network. IJCA Proceedings on National Conference on Innovative Paradigms in Engineering & Technology 2013 NCIPET 2013(8):8-12, December 2013. Full text available. BibTeX

	author = {Tanushree.selokar and Sanjay L. Badjate},
	title = {Article: Prediction of Net Bandwidth using Artificial neural Network},
	journal = {IJCA Proceedings on National Conference on Innovative Paradigms in Engineering & Technology 2013},
	year = {2013},
	volume = {NCIPET 2013},
	number = {8},
	pages = {8-12},
	month = {December},
	note = {Full text available}


Multi step prediction is a complex task that has attracted increasing interest in recent years. The contribution in this work is the development of nonlinear neural network models for the purpose of building multi step Prediction of Internet Bandwidth i. e. bits per second transmission record of server. It is observed that such problems exhibit a rich chaotic behavior and also leads to strange attractor. . This paper compares the performance of four neural network configurations namely a Multilayer Perceptron (MLP) , generalized feed forward network(GFF) , Self organized feature map (SOFM), and the Jorden –Elmen network with regards to various performance measures Mean square error (M. S. E. ),Normalized mean square error (N. M. S. E) and regression (r) . The standard back propagation algorithm with momentum term has been used for all the models. There are various parameters like number of processing elements, step size, momentum value in hidden layer, in output layer the various transfer functions like tanh, sigmoid, linear-tan-h and linear sigmoid, different error norms L1,L2 ,Lp to L infinity, Epochs variations and different combination of training and testing samples are exhaustively experimented for obtaining the proposed robust model for long term as well as short step ahead prediction.


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