control_examples

一个小练习，是我刚学MATLAB时边看边写的(A small practice, I am just learning MATLAB writing while watching the)

这个程序实现与Matlab的优化工具箱下，用于给图进行边着色。包含了很多图论的程序，如计算最大割集、连通度、最小覆盖边数等等…… (This program is implemented with Matlab Optimization Toolbox for graph edge coloring. Contains a lot of graph theory program, such as the calculation of the maximum cut sets, connectivity, minimal covering the number of edges, etc. ...)

实现脉冲消隐以在通信系统去除较大干扰,保证误码率性能(Realization of pulse blanking to remove larger interference in communication system, guaranting the bit error rate performance)

跟踪行人的matlab例子，仅供参考，亲测可以实现(Matlab example for tracking pedestrians for reference only)

这是先进PID控制书中的源码，是基于matlab的。(This is the advanced PID control of the source book is based on the matlab.)

This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters. (This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar-xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters. )

说明：  文章介绍了在OFDM系统中基于导频的信道估计方法(Article introduced the System in OFDM based on pilot channel estimation methods)

this code is regarding palm print recognition

说明：  如何用winrats做garch-bekk模型(how to use winrats creat garch-bekk)

Jacobi矩阵一般是坐标变换时用的——这个坐标系的坐标依次对那个坐标系的坐标求偏导数即得Jacobi矩阵。积分时从直角坐标变到球坐标、极坐标肯定要在积分号里乘上Jacobi矩阵的行列式。 若其特征值全为正，则此Jacobi矩阵正定，即它的任何二次型都大于零。并且如果是对称Jacobi矩阵，还可以实相合于一个单位阵。是比较强的条件。矩阵A的特征值是这个关于x的方程的所有根 det(A-xI)=0(Jacobi)