DIPUMToolbax

extreme learning machine 例子 run sinc_mean (extreme learning machine examples run sinc_mean)

说明：  实现协作频谱感知的瑞利衰落信道的and融合策略(Cooperative spectrum sensing in the Rayleigh fading channel and integration strategy)

移动通信信道的多径传播matlab仿真源代码(Mobile Communication Channel Multi-Drive transmission Matlab simulation source code)

《Matlab/Simulink通信系统建模与仿真实例分析学习辅导与习题详解》一书配套关盘，包含全部m文件、simulink模块(" Matlab/Simulink modeling and simulation of communication systems to learn counseling and case study exercises Comments," closed the book supporting disk, containing all the m file, simulink block)

HFSS_RF天线设计宝典。全彩页PDF。(hfss_rf anti design.all pdf)

MATLAB 关于数学的一些资料，方便大家的学习，可以下载(Some information about the mathematics MATLAB to facilitate everyone' s learning, you can download)

说明：  本人的一篇数字图像水印文章，一种基于图像最高有效位构造密钥的零水印算法（核心期刊），共大家参考。(A Kind of Non-watermarking Algorithm Based on MSB Structure Key )

bayesian classifier, naive bayesian classifier algorithms are implementer using matlab 2011

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. )

在MATLB中的OFDM的无线传输信道仿真。(Wiley10_MIMO-OFDM Wireless Communications with MATLAB )