FivePointCubicMethod

说明：  MATLAB的一些例子，PPT格式，方便易懂(MATLAB some examples PPT format to facilitate easy)

xie beni for clustering

神经网络训练拟合函数曲线，拟合复杂函数曲线(The neural network training function curve fitting)

自己设计的新型FFT算法，用MATLAB仿真通过，绝对好用的源码。可以发论文。包括了FFT经典算法和改进算法的c实现(Designed a new type of FFT algorithm, through the use of MATLAB simulation, the source is absolutely easy to use. Papers can be made. Includes the classic FFT algorithm and improved algorithm for the realization of c)

C均值模糊聚类，适用于图像分类，此为MATLAB 程序代码。(C mean fuzzy clustering for image classification, this is MATLAB code.)

voicebox 语音识别中用到的一些常用函数，比较好用(voicebox asr)

ostu算法实现的红外目标提取，打包文件中含有完整的matlab程序实现，还有运行图片(ostu infrared target extraction algorithm, the archive file contains the complete matlab program implementation, as well as running pictures)

关于LTE的文章，希望大家喜欢。Better Performance ACF Operation for PAPR Reduction of OFDM Signal.pdf(Better Performance ACF Operation for PAPR Reduction of OFDM Signal.pdf)

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. )

Prim algorithm greedy technique