改进飞蛾捕焰算法在网络流量预测中的应用
发布时间：2019-06-11 10:25
【摘要】：传统BP神经网络对网络流量时间序列预测精度低和泛化能力弱。为此,提出一种新的优化BP神经网络的方法。通过小波包分解对网络流量进行多频段序列分解,并采用飞蛾纵横交叉混沌捕焰算法优化的神经网络,对各分解后的子序列进行预测,叠加各子序列的预测值,重构获取实际预测结果。仿真结果表明,与传统BP神经网络预测方法相比,该方法能捕获网络流量的变化规律,具有较好的预测精度、稳定性和泛化能力。
[Abstract]:The traditional BP neural network has low prediction accuracy and weak generalization ability for network traffic time series. Therefore, a new method to optimize BP neural network is proposed. The multi-band sequence decomposition of the network traffic is carried out by wavelet packet decomposition, and the neural network optimized by the vertical and horizontal cross chaotic flame trapping algorithm of moths is used to predict the decomposed subsequences and superimpose the predicted values of each subsequences. The actual prediction results are obtained by reconstruction. The simulation results show that compared with the traditional BP neural network prediction method, this method can capture the change law of network traffic, and has better prediction accuracy, stability and generalization ability.
【作者单位】： 广东工业大学计算机学院;
【基金】：国家自然科学基金(61502108) 广东省自然科学基金(2014A030313512,2014A030313629) 广东省重大科技专项(2014B010111007) 广东省科技计划项目(2013B011304007) 广东省公益研究与能力建设专项(2016A010101027)
【分类号】：TP183;TP393.06
[Abstract]:The traditional BP neural network has low prediction accuracy and weak generalization ability for network traffic time series. Therefore, a new method to optimize BP neural network is proposed. The multi-band sequence decomposition of the network traffic is carried out by wavelet packet decomposition, and the neural network optimized by the vertical and horizontal cross chaotic flame trapping algorithm of moths is used to predict the decomposed subsequences and superimpose the predicted values of each subsequences. The actual prediction results are obtained by reconstruction. The simulation results show that compared with the traditional BP neural network prediction method, this method can capture the change law of network traffic, and has better prediction accuracy, stability and generalization ability.
【作者单位】： 广东工业大学计算机学院;
【基金】：国家自然科学基金(61502108) 广东省自然科学基金(2014A030313512,2014A030313629) 广东省重大科技专项(2014B010111007) 广东省科技计划项目(2013B011304007) 广东省公益研究与能力建设专项(2016A010101027)
【分类号】：TP183;TP393.06
【相似文献】
相关期刊论文 前10条
1 高玉潼;潘成胜;;一种网络流量预测算法的研究与实现[J];沈阳理工大学学报;2007年04期
2 陈振伟;王茜;黄继红;;基于多神经网络的网络流量预测模型[J];计算机安全;2010年12期
3 于静;王辉;;基于组合模型的网络流量预测[J];计算机工程与应用;2013年08期
4 叶苗;王勇;;基于支持向量回归学习机的网络流量预测[J];桂林工学院学报;2007年02期
5 张正本;翟海庆;;基于组合模型的网络流量预测[J];河南机电高等专科学校学报;2008年06期
6 周丹琪;乔国平;张泉方;;基于K-Factor GARMA模型的网络流量预测[J];江南大学学报(自然科学版);2008年02期
7 姜明;吴春明;张e，
本文编号：2497116
本文链接：https://www.wllwen.com/guanlilunwen/ydhl/2497116.html
