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Vol. 172, Issue 6, June 2014, pp. 224-228

 

Bullet

 

Network Traffic Prediction Using Radial Basis Function Neural Network Optimized
by Ant Colony Algorithm
 

Liu Jun, Guo Zuhua

Department of Computer Science of Henan Mechanical and Electrical Engineering College, Xinxiang Henan, 467002, China
Tel.: +86 18530228177

E-mail: ddnt@163.com

 

Received: 30 April 2014 /Accepted: 30 May 2014 /Published: 30 June 2014

Digital Sensors and Sensor Sysstems

 

Abstract: The disadvantages of the traditional radial basis function (RBF) neural network during the network traffic prediction process, such as a slow convergence rate and easy occurrence of local optima, result in low prediction precision. In this study, the ant colony optimization (ACO) algorithm is used to optimize the parameters of the RBF neural network for network traffic prediction. ACO is used to train the width and centre of the basis function of the RBF neural network, simplify the network structure, accelerate the convergence speed, prevent the occurrence of local optima, and improve the generalist ability of the RBF neural network. The experimental results show that compared with the genetic algorithm (GA)-RBF and particle swarm optimization (PSO)-RBF traffic prediction models, the proposed model exhibits higher prediction accuracy and can describe the varying trends in the network traffic well. The model used in this study exhibits strong generalization ability and good stability and therefore has practical value in network traffic prediction.

 

Keywords: Radial basis function (RBF) neural network, Ant colony algorithm, Basis function, Network traffic prediction.

 

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