It is important to choose proper wavelet functions in solving a practical problem using wavelet analysis.
用小波分析解决实际问题时,选择合适的小波函数是十分重要的。
参考来源 - 基于小波矩量法的PCB平面螺旋电感研究·2,447,543篇论文数据，部分数据来源于NoteExpress
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。
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