| 1. | Studying different problems by using different wavelet basis , we can obtain different results
不同的问题用不同小波基来分析研究，其效果会大不一样。 |
| 2. | 4 . have analyzed properties of wavelet coefficients obtained from an image through wavelet transform , and discussed how to select wavelet basis to optimize wavelet coefficients
( 4 )分析了图像小波变换后小波系数的特征，讨论了优化小波系数的小波基选择问题。 |
| 3. | Contrary to the fourier transform , the basis function used in wavelet is not exclusive . so a serious problem in engineering is that how to get the best wavelet basis , for different wavelet basis leads to different result
因此，小波分析在工程应用中一个十分重要的问题就是最优小波基的选择问题，因为用不同的小波基分析同一个问题会产生不同的结果。 |
| 4. | Mallat algorithm is deduced from the viewpoint of multi - resolution analysis . filter banks is used to construct orthogonal and biorthogonal wavelet basis . wavelet basis is selected according to the requirements of image compression
针对图像压缩这个具体应用给出小波基的选择依据，以及在尽可能好的重构原始图像的要求下，小波变换应当采用的处理方式。 |
| 5. | In order to handle problem that the number of wavelet basis functions grows exponentially with the number of the dimension of input space , two wavelet models are presented . the former is a wavelet network constructed by single - scaling multidimensional wavelet frames
针对小波函数个数与空间维数呈指数增长关系，而给多维空间中建模带来的困难，给出了两种小波模型。 |
| 6. | To take advantage of the excellent localization character of wavelet , cardinal b - splines wavelet basis is used as a substitute for the traditional polynomial basis in this dissertation . related theories are expatiated first , and then a method is developed for using wavelet in efg
本文首先对有关的理论作了阐述，然后提出了采用小波基的具体实现方法，并通过多个算例说明了此方法具有的高精度。 |
| 7. | We study the wavelet neural network theory and how to build models . the wavelet networks can be used in ecg signal compression by adjusting wavelet basis and weight values . at the same time , this algorithm also can reconstruct the ecg signal very well
在理论研究了小波变换方法和神经网络的基础上,提出了基于小波神经网络的ecg数据压缩算法,并分析研究了基于小波神经网络压缩ecg数据的原理和模型的构建方法。 |
| 8. | Artifical neural networks are employed for defect accurate recognition and calculation , the traditional bp neural network and wavelet basis function neural networks can successfully predict or estimate defect shape and geometry parameters , their application to the magnetic flux leakage inspection is put forward at first
将神经网络模式识别方法应用到缺陷漏磁检测中来，提出了用bp网络和小波神经网络对缺陷进行定量识别，精度高、效果好。 |
