This paper describes an Inverse Wavelet Transformation (IWT) approach to simulate target wind series with intermittency for given spectral functions as the input of structural wind responses analysis.
研究生成既满足给定的工程谱特性，又具有间歇性结构的风速序列作为结构气动计算的输入。
The system includes the following nine modules: pretreatment, wavelet analysis, spectral analysis, forward modeling, inversion, input, output, graph display and help.
系统包括预处理、小波分析、频谱分析、正演、反演、输入、输出、图形显示、帮助等九个模块。
The results show that this algorithm can model input and output learning kernel of dynamic nonlinear system quickly, which is superior to other learning methods of wavelet network.
结果表明该算法能够对动态非线性系统的输入输出快速学习和建模，优于其它小波网络的学习算法。
According to the method, the energy of different frequency bands after wavelet packet decomposition constitutes the input vectors of support vector machine as feature vectors.
该方法将振动信号小波包分解后的频带能量作为特征向量，输入到由多个支持向量机构成的多故障分类器中进行故障识别和分类。
After noise reduction in the signal, using "wavelet packet-energy" to extract the characteristic vector and input them to the neural network for fault identification.
在对采集到的信号降噪后，利用“小波包-能量”法提取特征量，并将其输入到神经网络中进行故障识别。
Based on the single scaling wavelet frame theory and radial basis function neural network, a multi dimensional input and output wavelet network is constructed.
在探索单尺度径向小波框架与径向基函数网络对函数逼近特性相似的基础上，构造了单尺度径向基小波网络。
The good localization characteristics of wavelet functions in both time and frequency space allowed hierarchical multi-resolution learning of input-output data mapping.
利用小波变换所具有的良好的时频分析特性，实现了输入输出之间映射关系的多分辨学习。
Firstly, the input image is decomposed using wavelet. Then sub-image which corresponds to high frequency in vertical direction is binarised. Finally, prior knowledge is used to locate the text region.
该算法首先对输入图像进行小波变换，在对小波变换后的垂直方向上的高频分量图进行二值化及规则限制。
The good localization characteristics of wavelet functions in both time and frequency space allow hierarchical multi-resolution learning of input-output data mapping.
由于小波变换在时间和频率空间具有良好的定位特性，使小波神经网络可对输入、输出数据进行多分辨的学习训练。
The good localization characteristics of wavelet functions in both time and frequency space allow hierarchical multi-resolution learning of input-output data mapping.
由于小波变换在时间和频率空间具有良好的定位特性，使小波神经网络可对输入、输出数据进行多分辨的学习训练。
应用推荐