clustering2

Fuzzy flip flop, basic code - an example

说明：  RBF模拟神经网络（主要用于函数拟合与模式分类）Matlab的示例程序(RBF simulated neural network (the main function for the fitting and pattern classification) Matlab sample program)

“Applied Econometrics using MATLAB”配套的计量经济Matlab包(MATLAB code for: 1. least-squares, simultaneous systems (2SLS,3SLS, SUR) 2. limited dependent variable (logit, probit, tobit) and Bayesian variants 3. time-series (VAR, BVAR, ECM) estimation and accompanying forecasting functions 4. ridge, Theil-goldberger, switching regimes, robust regression 5. regression diagnostics, e.g. Belsley, Kuh Welsch, Cook-Weisberg 6. cointegration testing 7. statistical distributions (CDF, PDF and random deviate generation) 8. Bayesian Gibbs sampling estimation and MCMC convergence diagnostics 9. maximum likelihood and Bayesian spatial econometrics functions 10. lots of other stuff, over 350 functions)

MATLAB编写的人脸识别程序，很经典的。(MATLAB face recognization)

数字波束形成的一些程序，适合入门级别，比较全，大家可以参考一下(some simple DBF programs,it s very good for the new learners)

基于C的Matlab函数功能扩充组合代码(Based on the C to expand the portfolio of Matlab Function code)

本程序主要是处理图像的，主要是用于求解被高幅度尖锐噪声而不是高斯分布噪声污染的信号分离问题~把问题矩阵分解成一个低秩表示的矩阵和一个稀疏矩阵，优化目标函数，用ALM方法是求解的~(This procedure is mainly to deal with the image, mainly for solving high-amplitude sharp noise rather than Gaussian noise distribution of the signal separation problem- the problem matrix is decomposed into a low-order matrix and a sparse matrix, optimization objective function, ALM method is the solution ~)

利用最小二乘法进行拟合多元非线性方程，用于特征降维，特征融合，相关分析等,基于chebyshev的水声信号分析。( Multivariate least squares fitting method of nonlinear equations, For feature reduction, feature fusion, correlation analysis, Based chebyshev underwater acoustic signal analysis.)

butterworth滤波器，可实现高通低通滤波(butterworth filter, low pass filter high-pass)

试用时域最小平方误差准则（最小平方逆设计）设计一个具有四项系数的IIR DF的系统函数，使其在y(n)=[3,2,1]输入激励下，输出v(n)逼近d(n)=[2,0.2,0.05]。令v(-1)=v(-2)=0。求出v(n)的前8个样值与d(n)进行比较。用matlab实现了此要求。(trial at the least square error domain guidelines (minimum inverse square design) design with a four DF IIR coefficient of the system function, so in y (n) = [3,2,1] incentive to import, export v (n) approximation d (n) = [2,0.2,0.05]. So v (1) = v (2) = 0. Get v (n) of eight samples value d (n) comparisons. Using Matlab realization of this request.)