vulnerable_watermark

Fast Wavelet Transform

小波神经网络实现短期电力负荷预测，神经网络模型及其BP学习算法。 (Wavelet neural network short term load forecasting, neural network model and BP learning algorithm.)

这篇关于从心电信号提取呼吸信号的调查，一共介绍了三种方法，即：频率提取，滤波器滤波，小波变换，三种方法。(This extract on the respiratory signal from ECG investigation, presented a total of three methods, namely: the frequency extraction filter filtering, wavelet transform, three ways.)

说明：   这是一个用小波神经网络进行非线性函数逼近的例子，拟合效果非常好，保证能运行！有详细注释！我是参考《青岛海洋大学学报》 2001年第1期 一种基于BP算法学习的小波神经网络编写的，大家可以针对原文更好的理解我的程序！(This is a wavelet neural network with nonlinear function approximation example of fitting results is very good, guaranteed to run! Detailed Notes! I refer to Journal of Ocean University of Qingdao，2001 ，“a learning algorithm based on BP wavelet neural network ”, you will understand me better for program!)

小波变换测试，小波分解的级数设置为3级，可以实现数字图像的小波变化和逆变化。测试代码请使用512*512灰度图像(The wavelet transformation test, wavelet decomposition series is set to 3, digital image wavelet changes and reverse changes.)

本文通过比较第一代和第二代小波算法特点,引入二代小波提升结构的概念,提出了一种基于二代小波提升结构的快速图像边缘检测算法 对三次B样条小波基实现提升格式,通过计算大尺度下分解子图的模值和幅角来确定边缘 经过实验比较,能比经典的边缘检测算法得出更精确的边缘图像,同时通过与基于第一代小波算法的边缘检测比较,基于二代小波提升格式的边缘检测算法计算更快速,更高效。(In this paper, by comparing first-and second-generation wavelet algorithm features, the introduction of second generation wavelet to enhance the concept of structure, a second generation wavelet-based structure to enhance the rapid image edge detection algorithm on the three B-spline wavelet lifting scheme to achieve through Calculation of large-scale mold subgraph decomposition value and increase the edge angle to determine the experimental comparison, than the classical edge detection algorithm to draw a more accurate edge image, at the same time with the first generation of wavelet-based edge detection algorithm comparison, based on the Second generation wavelet lifting scheme edge detection algorithm is faster and more efficient.)

使用小波包分析信号的MATLAB程序，对初学者有用~~~~·( analysis Signal by Wavelet packet..matlab~~)

根据小波变换后的模极大值 重建原始信号(To reconstruct the original signal based on wavelet transform modulus maxima)

用小波方法把脑电信号进行频段分解，并画出图形(Using wavelet method to decompose the eeg signals into frequency bands, and draw the graph)

1、求冲激函数、阶跃函数及三角函数的小波变换 2、模极大值曲线 3、求它们的李氏指数(one, seeking impulse function, step function and trigonometry two wavelet transform, modulus maxima curve 3, Lee seeking their index)