miller_simulink

this program solves an Income Fluctuation Problem requires obj.m function which is the RHS of the Bellman equation as a function of c=consumption and x=cash in hand

关于C_C方法的混沌时间序列相空间重构matlab(About C_C chaotic time series phase space reconstruction method matlab)

设r是f(x) = 0的根，选取x0作为r初始近似值，过点（x0,f(x0)）做曲线y = f(x)的切线L，L的方程为y = f(x0)+f (x0)(x-x0)，求出L与x轴交点的横坐标 x1 = x0-f(x0)/f (x0)，称x1为r的一次近似值。过点（x1,f(x1)）做曲线y = f(x)的切线，并求该切线与x轴交点的横坐标 x2 = x1-f(x1)/f (x1)，称x2为r的二次近似值。重复以上过程，得r的近似值序列，其中x(n+1)=x(n)－f(x(n))/f (x(n))，称为r的n+1次近似值(Let r is f (x) = 0 root, select the initial approximation x0 as the r, over point (x0, f (x0)) to do the curve y = f (x) the tangent L, L the equation y = f ( x0)+ f ' (x0) (x-x0), find the intersection of L and the x-axis of abscissa x1 = x0-f (x0)/f' (x0), x1 is called an approximation r. Through points (x1, f (x1)) to do the curve y = f (x) the tangent, and find the intersection of the tangent with the x-axis of abscissa x2 = x1-f (x1)/f ' (x1), x2 is called r the second approximation. Repeat the process, get an approximation of the sequence r, where x (n+1) = x (n)-f (x (n))/f ' (x (n)), as an approximation of r n+1 times)

修改后的phase函数，把它的门限值换为pi，使用更方便(The modified phase function, the threshold for it to pi, the use of more convenient)

别人开发俄罗斯代码，很有用，拿来分享！欢迎使用！ (Others to develop the Russian code, useful to them and share! Welcome!)

This code is about adaptive autoregressive. you can find AR coefficient.

基于非局部均值图像去噪，对高斯加噪声有很好的去除效果，其中的修改程序，加入了对称处理，处理对称图像效果更好(Based on non-local means image denoising, the high Alaska noise removal, which modify the program, joined the symmetrical treatment, processing symmetrical image effect better)

说明：  这是一个关于控制仿真的程序，但是作用不是很大 仅供参考(This is a program about control simulation, but it is not very useful for reference only.)

Face feature extraction

MATLAB提供了各种曲线拟合方法，例如：Exponential, Fourier, Gaussing, Interpolant, Polynomial, Power, Rational, Smoothing Spline, Sum of Functions, Weibull等，当然，也可以使用 Custom Equations. cftool不仅可以绘制拟合后的曲线、给出拟合参数，还能给出拟合好坏的评价参数(Goodness of fit)如SSE, R-square, RMSE等数据，非常好用。但是如果我们已经确定了拟合的方法，只需要对数据进行计算，那么这种GUI的操作方式就不太适合了，比如在m文件中就不方便直接调用cftool。 (matlab has given a lot of method for curve combine ,we will give a new method and provide a matlab code.)