simulink_123

MATLAB扩展编程MATLABExtendedProgramming(MATLAB Extended Programming)

集合了多种频谱分析方法，利用matlab进行编程分析，文件含有多种代码。(Collection of a variety of spectral analysis method , using matlab programming analysis , the file contains a variety of code )

Shielding effectiveness of thickness of copper and alumunium layer

已知自变量t，因变量y，y和t满足一下函数式： dy/dt=0.001414/(1/(4.5727*10^4*k1*(1-0.02119*y)^k2)+1/k3/45727)；（需要指出的是，该公式中参数可进一步合并，在本帖中并未作合并处理） 已知 t=；y=；拟合出参数k1，k2，k3。(Known independent variable t, the dependent variable y, y, and t satisfy my functional: dy/dt = 0.001414/(1/(4.5727* 10 ^ 4* k1* (1-0.02119* y) ^ k2)+1/k3/45727) (It should be noted that the equation parameters can be further consolidated in this post are not treated as a merger) is known t = y = fitting parameters k1, k2, k3.)

逐次订正法差值程序，可代替matlab自带的griddata(Successive correction method difference program, instead of Matlab comes griddata)

时间序列数据分析中的梅林变换工具，小波包分析提取振动信号中的特征频率，算法优化非常好，几乎没有循环。( Time series data analysis Mellin transform tool, Wavelet packet analysis to extract vibration signal characteristic frequency, Algorithm optimization is very good, almost no circulation.)

支持向量机的matlab代码，根据自己需要修改即可(Support vector machine matlab code can be modified according to their needs)

关于系统的神经网络的matlab辨识问题(system identification matlab about neural network)

说明：  按FPE定阶的 源程序：fpe.cpp M序列：M序列.txt 白噪声：Gauss.txt 程序中先用依模型阶次递推算法估计模型的参数，再用fpe方法判断模型的阶次。 程序运行结果如下： n: 1 判断阶次FPE的值: 0.0096406 -0.481665 1.07868 n: 2 判断阶次FPE的值: 0.00875755 -0.446739 0.00498181 1.07791 0.0527289 n: 3 判断阶次FPE的值: 0.0087098 -0.459433 0.120972 -0.0569228 1.07814 0.0390757 0.116982 n: 4 判断阶次FPE的值: 0.000396884 -0.509677 0.4501 -0.200906 0.0656188 1.07991 -0.0156362 0.442989 0.0497236 n: 5 判断阶次FPE的值: 3.2095e-007 -1.18415 0.813123 -0.517862 0.34881 -0.116864 1.07999 -0.744141 0.474462 -0.253112 0.122771 n: 6 判断阶次FPE的值: 3.23349e-007 -1.14659 0.76933 -0.487651 0.329676 -0.10377 -0.00440907 1.07999 -0.703574 0.447253 -0.235282 0.113587 0.00479688 从以上结果可以看出，当n=5时，fpe值最小，所以这时的模型阶次和参数估计值为最优结果： 3.2095e-007 -1.18415 0.813123 -0.517862 0.34881 -0.116864 1.07999 -0.744141 0.474462 -0.253112 0.122771(err)

Digital Watermarking Using Discrete Wavelet Transform (DWT) & Singular Value Decomposition (SVD) & Checking its Robustness in the presence of JPEG Image Compression