CAJ | 학술논문

为了改善小波神经网络（WNN）进行流量预测的性能及避免量子粒子群算法（QPSO）搜索后期的早熟收敛缺陷，提出了一种改进的 QPSO。该算法定义粒子群聚拢度，改进收缩—扩张系数使其表示为聚拢度的函数并服从随机分布，以使粒子群具有动态自适应性，避免陷入局部最优，并通过搜索使用 WNN 待优化参数编码位置向量的粒子群的全局最优位置来实现目标参数的优化，使用本算法优化 WNN 参数，建立了基于改进的 QPSO优化 WNN 的网络流量预测模型。使用真实网络流量通过两组对比实验对其预测精度进行验证，证明了该方法的可用性。实验结果表明，该方法的预测精度优于 WNN 和 QPSO-WNN 方法。
위료개선소파신경망락（WNN）진행류량예측적성능급피면양자입자군산법（QPSO）수색후기적조숙수렴결함，제출료일충개진적 QPSO。해산법정의입자군취롱도，개진수축—확장계수사기표시위취롱도적함수병복종수궤분포，이사입자군구유동태자괄응성，피면함입국부최우，병통과수색사용 WNN 대우화삼수편마위치향량적입자군적전국최우위치래실현목표삼수적우화，사용본산법우화 WNN 삼수，건립료기우개진적 QPSO우화 WNN 적망락류량예측모형。사용진실망락류량통과량조대비실험대기예측정도진행험증，증명료해방법적가용성。실험결과표명，해방법적예측정도우우 WNN 화 QPSO-WNN 방법。
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 wave-let 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).