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.
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 |
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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: | http://usir.salford.ac.uk/id/eprint/41042 |
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