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。
Secondly, this paper summarizes the current main methods of harmonic detection and puts forward a harmonic detection method based on wavelet neural network (WNN).
其次，总结了当前主流的谐波检测方法，提出了一种基于小波神经网络的谐波检测方法。
Structure model and algorithms of Wavelet Neural Network (WNN) are designed combining the advantages of both wavelet transform and Artificial Neural Network (ANN).
结合小波变换和神经网络的优势给出小波神经网络的结构模型，研究了小波神经网络的学习算法；
Structure model and algorithms of Wavelet Neural Network (WNN) are designed combining the advantages of both wavelet transform and Artificial Neural Network (ANN).
结合小波变换和神经网络的优势给出小波神经网络的结构模型，研究了小波神经网络的学习算法；
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