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Math. Comput. Appl. 2014, 19(3), 218-229; doi:10.3390/mca19030218

A Network Traffic Prediction Model Based on Quantum Inspired Pso and Wavelet Neural Network

Department of Mathematics, Chuxiong normal university, 675000, Chuxiong, Yunnan, China
Published: 1 December 2014
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Abstract

Network traffic flow prediction model is fundamental to the network performance evaluation and the design of network control scheme which is crucial for the success of high-speed networks. Aiming at shortcoming of the conventional network traffic time series prediction model and the problem that BP training algorithms easily plunge into local solution, a network traffic prediction model based on wavelet neural network and PSO-QI is presented in the paper. Firstly, the quantum principle obtained from Quantum PSO(QPSO)has been combined with standard PSO to form a new hybrid algorithm called PSO with Quantum Infusion(PSO-QI). Then, the parameters of wavelet neural network were optimized with PSO-QI and the time series of network traffic data was modeled and predicted based on wavelet neural network and PSO-QI. Experiments showed that PSOQI-wavelet neural network has better precision and adaptability compared with the traditional neural network.
Keywords: BP neural network; particle swarm optimization; PSO-QI algorithm; wavelet network traffic BP neural network; particle swarm optimization; PSO-QI algorithm; wavelet network traffic
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhang, K. A Network Traffic Prediction Model Based on Quantum Inspired Pso and Wavelet Neural Network. Math. Comput. Appl. 2014, 19, 218-229.

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