propagDifferentMed

该程序分别用四种经典算法对自定义3次非线性函数寻优，并给出寻优路径(The program of four classic algorithms were used three times on custom non-linear function optimization, and gives the path optimization)

intersection_lines(A, B) between two lines

学习MATLAB的参考资料，对于初学者，可以参考里面的讲解实例。(Learning MATLAB reference, for beginners, can explain the instance reference inside.)

图像处理PPT，适合初学者，介绍各种图像处理方法(Image processing PPT, suitable for beginners, introducing the various image processing methods)

lectures for singular spectrum analysis

Matlab 程序实现现代功率谱估计。包含自相关法，burg法，改进协方差法。(A m file using modern spectrum estimation ,including Autocorrelation metohd and Burg method, and modified Covariance method.)

宽带信号的DOA估计算法，可以实现宽带信号的doa估计(doa estimation algorithm for wide signal )

Introduction to Simulink with Engineering Applications.

汽车高斯曲面拟合 --- 2程序，以适应到表面二维高斯： 子= A *的进出口（ -（（西为X0）^2/2/sigmax^2 +（艺Y0的）^2/2/sigmay^ 2）。。）+ b的 这些例程是自动在某种意义上说，他们并不需要出发对模型参数的猜测规范。 autoGaussianSurfML（十一，彝，子）适合通过对模型参数的最大似然（最小二乘）。它首先计算了该模型在许多可能的参数值，然后选择最佳质量设置和细化与lsqcurvefit它。 autoGaussianSurfGS（十一，彝，紫）的估计，通过指定数据的贝叶斯生成模型，然后采取通过从模型吉布斯抽样样本后ofthis模型参数。这种(Auto Gaussian Surface fit --- 2 routines to fit a 2D Gaussian to a surface: zi = a*exp(-((xi-x0).^2/2/sigmax^2+ (yi-y0).^2/2/sigmay^2))+ b The routines are automatic in the sense that they do not require the specification of starting guesses for the model parameters. autoGaussianSurfML(xi,yi,zi) fits the model parameters through maximum likelihood(least-squares). It first evaluates the quality of the model at many possible values of the parameters then chooses the best set and refines it with lsqcurvefit. autoGaussianSurfGS(xi,yi,zi) estimates the model parameters by specifying a Bayesian generative model for the data, then taking samples from the posterior ofthis model through Gibbs sampling. This method is insensitive to local minimain posterior and gives meaningful error bars (Bayesian confidence intervals))

LMS算法实现自适应滤波 clear close all clc N=10000 设置仿真长度 信号产生参数设定 a1=-0.195 a1=-1.5955 a2=0.95 R0=[1,a1,a2 a1,1+a2,0 a2,a1,1] p=[1,0,0] r=inv(R0)*p 计算理论自相关函数 R=[r(1),r(2) r(2),r(1)] 生成理论自相关矩阵 p1=[r(2),r(3)] 生成互相关 h=inv(R)*p1 计算维纳解 Jmin=r(1)-h *p1 计算维纳解时最小均方误差 u=1/sum(eigs(R)) ( LMS算法实现自适应滤波 clear close all clc N=10000 设置仿真长度 信号产生参数设定 a1=-0.195 a1=-1.5955 a2=0.95 R0=[1,a1,a2 a1,1+a2,0 a2,a1,1] p=[1,0,0] r=inv(R0)*p 计算理论自相关函数 R=[r(1),r(2) r(2),r(1)] 生成理论自相关矩阵 p1=[r(2),r(3)] 生成互相关 h=inv(R)*p1 计算维纳解 Jmin=r(1)-h*p1 计算维纳解时最小均方误差 u=1/sum(eigs(R)) )