Integrating the merit of wavelet transform with that of artificial neural network, a wavelet neural network (WNN) model for forecasting network traffic was created.
结合小波变换和人工神经网络的优势,建立一种网络流量预测的小波神经网络模型。
Aiming at the issue about multi-step prediction of the traffic flow chaotic time series, a fast learning algorithm of wavelet neural network (WNN) based on chaotic mechanism is proposed.
针对交通流量混沌时间序列多步预测的问题,提出了一种基于混沌机理的小波神经网络(WNN)快速学习算法。
The paper proposes application of Wavelet Neural Network in high-frequency time series calendar effects' study. At last, the paper proves that WNN is better than classical FFF regression.
提出了用小波神经网络(WNN)来定量研究高频金融时间序列“日历效应”,通过比较发现WNN 是比弹性傅立叶形式(FFF)回归技术更具优势的方法。
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