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Reseach Article

Prediction of Net Bandwidth using Artificial neural Network

Published on December 2013 by Tanushree.selokar, Sanjay L. Badjate
National Conference on Innovative Paradigms in Engineering & Technology 2013
Foundation of Computer Science USA
NCIPET2013 - Number 8
December 2013
Authors: Tanushree.selokar, Sanjay L. Badjate
e122e590-48ec-4a88-adb9-6872d76180bd

Tanushree.selokar, Sanjay L. Badjate . Prediction of Net Bandwidth using Artificial neural Network. National Conference on Innovative Paradigms in Engineering & Technology 2013. NCIPET2013, 8 (December 2013), 8-12.

@article{
author = { Tanushree.selokar, Sanjay L. Badjate },
title = { Prediction of Net Bandwidth using Artificial neural Network },
journal = { National Conference on Innovative Paradigms in Engineering & Technology 2013 },
issue_date = { December 2013 },
volume = { NCIPET2013 },
number = { 8 },
month = { December },
year = { 2013 },
issn = 0975-8887,
pages = { 8-12 },
numpages = 5,
url = { /proceedings/ncipet2013/number8/14746-1440/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Innovative Paradigms in Engineering & Technology 2013
%A Tanushree.selokar
%A Sanjay L. Badjate
%T Prediction of Net Bandwidth using Artificial neural Network
%J National Conference on Innovative Paradigms in Engineering & Technology 2013
%@ 0975-8887
%V NCIPET2013
%N 8
%P 8-12
%D 2013
%I International Journal of Computer Applications
Abstract

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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Index Terms

Computer Science
Information Sciences

Keywords

Chaotic Multi Step Prediction Cross Validation.