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首页> 外文期刊>Discrete dynamics in nature and society >A Network Traffic Prediction Model Based on Quantum-Behaved Particle Swarm Optimization Algorithm and Fuzzy Wavelet Neural Network
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A Network Traffic Prediction Model Based on Quantum-Behaved Particle Swarm Optimization Algorithm and Fuzzy Wavelet Neural Network

机译：一种基于量子表现粒子群优化算法和模糊小波神经网络的网络流量预测模型

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摘要

Due to the fact that the fluctuation of network traffic is affected by various factors, accurate prediction of network traffic is regarded as a challenging task of the time series prediction process. For this purpose, a novel prediction method of network traffic based on QPSO algorithmand fuzzy wavelet neural network is proposed in this paper. Firstly, quantum-behaved particle swarmoptimization (QPSO) was introduced. Then, the structure and operation algorithms of WFNN are presented.The parameters of fuzzy wavelet neural network were optimized by QPSO algorithm. Finally, the QPSO-FWNN could be used in prediction of network traffic simulation successfully and evaluate the performance of different prediction models such as BP neural network, RBF neural network, fuzzy neural network, and FWNN-GA neural network. Simulation results show that QPSO-FWNN has a better precision and stability in calculation. At the same time, the QPSO-FWNN also has better generalization ability, and it has a broad prospect on application.
机译：由于网络流量的波动受到各种因素的影响，对网络流量的准确预测被认为是时间序列预测过程的具有挑战性的任务。为此，提出了一种基于QPSO算法和模糊小波神经网络的网络流量的新型预测方法。首先，介绍了量子表现粒子培养基（QPSO）。然后，介绍了WFNN的结构和操作算法。通过QPSO算法优化了模糊小波神经网络的参数。最后，QPSO-FWNN可以成功地用于预测网络流量模拟，并评估不同预测模型的性能，如BP神经网络，RBF神经网络，模糊神经网络和FWNN-GA神经网络。仿真结果表明，QPSO-FWNN在计算中具有更好的精度和稳定性。与此同时，QPSO-FWNN还具有更好的泛化能力，并且它具有广阔的应用前景。

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