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Adaptive tracking system is to track the video frames

说明：  经典的遗传算法工具包. 可以实现用户需要的多数遗传算法操作功能.(Classical genetic algorithm toolkit.Enables users need to operate most of the genetic algorithm function.)

DESIGN and IMPLEMENTATION of a SHUNT ACTIVE POWER FILTER with REDUCED DC LINK VOLTAGE

matlab examples from university of singapore

预测控制里面MAC算法的实现，简单明了，欢迎交流(programming for prediction control)

    近些年，在机器学习（Machine Learning）的各领域里，出现了一个非常有用的基本问题结构：亚模性（子模性，次模性）（submodularity）是一个直觉上收益递减（diminishing return）的性质，声明给一个小集合增加元素比给一个大集合增加同一个元素更有帮助。与凸性（convexity）类似，亚模性可以有效找出大规模问题的可证明的（近似）最优解。    SFO：一个MATLAB/Octave工具箱，实现各种用于亚模函数最小化/最大化的算法。   相关论文发表在Journal of Machine Learning  Research（JMLR）11（2010）1141-1144   【注】本人已将该论文添加在源代码包之中。     希望您使用愉快！^-^ 

matlab函数编写与调用  ,跟其它的编程语言都一样，但是子函数与主函数要存于不同的文件中，文件名就是函数名字。文件必须保存在current directory中，才能调用。

potential field method

说明：  MATLAB的仿真程序,关于TOKEN的,仿真了TOKEN这个MAC方式的运行.这里是.M文件(MATLAB simulation program on TOKEN. TOKEN simulation of the way the MAC operation. here yes. M paper)

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. )