The-calculation-method

Matlab的英文原版Simulink仿真的参考教程…… (Matlab Simulink Reference，Hope it was useful for someone...)

说明：  MVDR波束形成的MATLAB仿真实现。(MVDR beamforming MATLAB Simulation.)

改良后的下垂控制，效果还行，仅供参考.用于并网逆变器控制。(Droop control improved, and the results were OK, for reference purposes only. Used in grid inverter control.)

cognitive radio, spectrum sensing, spectrum allocation

MATLAB命令大全 Matlab常用的命令，按照字母顺序排列的，带解释，比较实用。(MATLAB command Matlab command, in alphabetical order, with explained.)

全称是：MIL learners and their ensemble versions一个很好的lazy learnng matlab 程序(MIL learners and their ensemble versions)

matlab中的各种语言的含义几使用规则(matlab meaning in several languages using the rules)

太阳能电池板VI曲线建模 用matlab的simulink工具 可视曲线(Simulink tool solar panels VI curve modeling using matlab visual curve)

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. )

conversion color spaces