wavelet_entropy

用matlab编写的雅克比方法求解线性方程组：数值分析中很经典的代码(a procedure of jacobi diedai)

matlab常用命令 (matlab order)

说明：   可以参考，对做通信课题设计的人来说，看了很有益处(can refer to the communication issue so those who design and useful read)

Single input single output system

1．用Matlab产生正弦波,矩形波,以及白噪声信号，并显示各自时域波形图 2．进行FFT变换，显示各自频谱图，其中采样率，频率、数据长度自选 3．做出上述三种信号的均方根图谱,功率图谱,以及对数均方根图谱 4．用IFFT傅立叶反变换恢复信号，并显示恢复的正弦信号时域波形图(1. Produce sine wave, square wave, and the white noise signal with Matlab, and their time-domain waveform display 2. FFT transform, display each spectrum, where the sampling rate, frequency, length optional data 3. RMS to make maps of the three signals, power spectrum, as well as the number of RMS map 4. Restored by inverse Fourier transform IFFT signal and displays the time domain waveforms sinusoidal signal recovery)

The radially averaged power spectrum (RAPS) is the direction-independent mean spectrum, i.e. the average of all possible directional power spectra. This function computes and plots the RAPS of an input image. The function was written to produce the RAPS for square weather radar reflectivity images so you will have to relabel the plot axes and title to suit your needs. The radially averaged power spectrum provides a convenient means to view and compare information contained in 2D spectra in 1D

clasificacion de firmas

说明：  在学习优化算法的过程中，为快速了解算法求解过程，整理出这两个算法。通过蚁群算法和萤火虫算法对测试函数进行求解，以图片的形式展示算法迭代过程中蚂蚁或萤火虫的移动过程，了解算法特点。相对而言蚁群算法更快，但不一定能找到最优解。(By learning the optimization algorithm, the two algorithms are sorted out. The ant colony algorithm and the firefly algorithm are used to solve the test function. The moving process of the ant or firefly in the iterative process of the algorithm is shown in the form of pictures. To understand the characteristics of the algorithm, the ant colony algorithm is relatively faster, but it is not necessarily able to find the optimal solution.)

说明：  使用各种牛顿法（包括普通牛顿法，拟牛顿法，阻尼牛顿法）求解优化问题(Using various Newton methods (including general Newton method, quasi Newton method, damped Newton method) to solve optimization problems)

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