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- Title
Internet traffic forecasting based on wavelet neural network optimized by improved quantum-behaved particle swarm optimization.
- Authors
MENG Fei; LAN Ju-long; HU Yu-xiang
- Abstract
To improve the performance of wavelet neural network (WNN) model in forecasting network traffic, as well as to avoid the shortcomings of premature convergence of quantum-behaved particle swarm optimization (QPSO) algorithm, this paper proposed a novel improved IQPSO method. This method defined particle gathering degree and improved contraction-expansion coefficient, which was subject to stochastic distribution, to be expressed as the function of particle gathering degree to make swarm have self-adaption, avoiding falling into local optimum. And by searching for the global best particle, it optimized wavelet neural network parameters which were encoded in the positions of particles. It trained the wavelet neural network with IQPSO to implement the optimization of WNN parameters and established the network traffic forecasting model based on the wavelet neural network optimized by improved quantum-behaved particle swarm optimization (IQPSO-WNN). Forecasting results on real network traffic demonstrate that the prediction accuracy of the proposed method is more accurate than that of traditional wavelet neural network and wavelet neural network optimized by quantum-behaved particle swarm optimization(QPSO-WNN).
- Publication
Application Research of Computers / Jisuanji Yingyong Yanjiu, 2015, Vol 32, Issue 5, p1450
- ISSN
1001-3695
- Publication type
Article
- DOI
10.3969/j.issn.1001-3695.2015.05.042