A novel neural computing model for fast predicting network traffic

Liu, Q, Cai, W, Shen, J, Fu, Z and Linge, N ORCID: https://orcid.org/0000-0002-4318-8782 2015, 'A novel neural computing model for fast predicting network traffic' , Journal of Computational and Theoretical Nanoscience, 12 (12) , pp. 6056-6062.

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Abstract

Currently existing web traffic prediction models have the shortages of low accuracy, low stability and slow training speed. Aiming at such problems, this paper proposes a new model to predict the network traffic called MRERPM (MapReduce-based ELM Regression Prediction Model). In this prediction model, Extreme Learning Machine is used to accelerate the training speed and improve the accuracy of prediction. Moreover, a distributed cluster is established based on Apache Hadoop to furtherly improve the processing capacity. Experiment results show that MRERM has a large improvement over training speed compared with other models based on K-ELM or SVR, but not at the cost of accuracy.

Item Type:	Article
Schools:	Schools > School of Computing, Science and Engineering
Journal or Publication Title:	Journal of Computational and Theoretical Nanoscience
Publisher:	American Scientific Publishers
ISSN:	1546-1955
Depositing User:	USIR Admin
Date Deposited:	16 Dec 2016 13:37
Last Modified:	27 Aug 2021 20:35
URI:	https://usir.salford.ac.uk/id/eprint/41042

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