Both the wavelet functions and the least square algorithm of fitting of data are used to construct a new method of fitting of curve and surface.
本文把小波函数引入离散数据拟合领域，将小波函数与数据拟合的常用方法——最小二乘法相结合，给出了一种新型的数据拟合工具。
The results showed that when different wavelet functions were used to analyze the same pressure wave signal, the differences could be found between each other.
研究结果表明：选用不同的小波函数对同一压力波信号进行时频特性分析时，得出的结果是有差别的。
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 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。
In this paper, six wavelet functions were selected for the analysis of time-frequency characteristics of the pressure wave signal in fuel injection system of a diesel engine.
通过选用六种不同的小波函数，对柴油机喷油系统压力波信号的时频特性进行了小波分析研究。
Finally, the influence of different attacks, the various resolution levels, the intensity of watermarking and the different wavelet functions on digital image watermarking are researched.
最后，通过采用几种攻击方法、不同的小波分解层数、水印嵌入强度、不同小波基函数的选取来检测其对水印的影响。
By comparison with other wavelet functions, trapezoid complex wavelet function has even frequency characteristic, and so trapezoid complex wavelet transform easily catch frequency deviation.
梯形小波函数具有平坦的频率特性，因而梯形小波变换较容易捕捉到信号的频率偏移。
The complex wavelet functions constructed by means of this method have advantages of good performance in frequency localization and better veracity in extracting characteristic parts of signals.
该方法构造的复值小波具有频率局部化性能好、取故障信号特征分量较准确的优点。
Furthermore, in order to make a necessary foundation for further research, the relevant knowledge about wavelet analysis and generalized functions is introduced briefly.
并简要介绍小波分析及广义函数的一些基本原理与相关知识，以此作为本课题研究的必备基础。
Wavelet is the best base of functions, with point singularity, and it has wide application in image processing because of its time-frequency localization and multiscale features.
小波是表示具有点奇异性函数的最优基，它由于具有时-频局部化特点和多尺度特性，在图像处理领域得到了广泛应用。
General functions may be represented as Wavelet series. A kind of stabile, efficient and fast algorithm of Wavelet transform may be gotten through multi-resolution analysis.
一般函数都可以写成小波级数的形式，而由多分辨分析可得到小波的稳定有效快速变换算法。
Wavelet theory provides various basis functions and multi-resolution methods for finite element method, which will be selected in solving different problems.
小波理论为有限元方法提供了许多不同的基函数和多尺度分析方法，需要根据具体分析问题进行选择。
By applying wavelet analysis method to image edge detection, multi scale operator of wavelet analysis functions well for noise resisting ability and picture edge reserving.
将小波分析方法应用于图像边缘检测领域，小波分析的多尺度算子在抗噪声和保留图像边缘的能力上有比较好的效果。
The paper USES wavelet neutral network to build up warning model, and judges its forecast results, then draws a conclusion that wavelet neutral network has many superior functions in the warning.
利用小波神经网络预警方法建立预警模型，并判断其预测效果，由此分析出小波神经网络在预警方面具有诸多优越的性能。
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。
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。
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