wind_pmsg-turbin

德鲁德模型拟合，通过调节参数与金属银的介电常数实验参数拟合，得到可见光和近红外波段合适的drude模型参数(Drude model fitting, the right of the visible and near infrared bands drude model parameters by adjusting the parameters and the dielectric constant of the metal silver experimental parameter fitting)

本程序可以用于自然最近邻的搜索,这里以500个点威力(This procedure can be used to naturally nearest neighbor search)

基于matlab中simulink的频率合成器的设计，主要结构包括鉴相器，低通滤波器，压控振荡器(simulink based franquence synthesis,consisted of pdf,lpf,vco)

实现分选PRI抖动雷达脉冲的功能，包含一个主函数main1和三个子函数jicha,tjbox,sousuo(achieving PRI jitter radar pulse deinterleaving,including one main function main1 and three sub functions named jicha,tjbox and sousuo)

变流器的预测控制，模型搭建成功，仿真结果符合要求。(Converter predictive control, built a successful model, the simulation results meet the requirements.)

BPSK simulation with Matlan

在matlab开发环境下，实现对直流电动机调速系统的仿真(MATLAB development environment to achieve the simulation of DC motor speed control system)

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. )

Kalman Filtering- Theory and Practice Using MATLAB 2008年第三版的随书源码(Kalman Filtering-Theory and Practice Using MATLAB the third edition CD)

Free Split and Merge Expectation-Maximization algorithm for Multivariate Gaussian Mixtures. This algorithm is suitable to estimate mixture parameters and the number of conpounds