The simulation results on real network traffic show that WNN model is more successful than...
用实际网络流量对该模型进行验证，结果表明，该模型具有较高的预测效果。
WNN can be seen as a classifier to distinguish the corrupted or uncorrupted pixels from others in both approaches.
在两种方案中，WNN都可以看作是一个区分污染与未污染像素的分类器。
Using MRA-WNN, we can approach the whole developing trend of the stock market (the contour), and also capture the changing details.
应用MRA-WNN既能逼近股票市场的整体变化趋势（轮廓），亦能捕捉变化的细节。
Then the mechanism of the chaotic learning algorithm is described, and the adaptive learning algorithm of WNN for traffic flow time series is designed.
阐述了混沌学习算法的机理，设计了交通流量WNN混沌时间序列自适应学习算法。
In WNN the most fast grads descent methodology was adopted to adjust the network parameters and the learning rate by self adapting learning rate method.
对小波神经网络采用最速梯度下降法优化网络参数，并对学习率采用自适应学习速率方法自动调节。
WNN controller with the idea of neural networks inverse control is applied. Using multi-step predictive index function to train the weights of controller.
采用神经网络逆控制的思想设计小波神经网络控制器，引入多步预测性能指标函数对控制器权值进行在线训练。
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；
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。
It is also indicated that current WNN has a poor convergence performance because of adopting the random initialization method and gradient training algorithm of traditional BP NET.
还指出由于当前的连续小波神经网络主要使用传统BP神经网络的随机初始化方法和基于梯度的训练算法，因此存在收敛性差的缺点。
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。
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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