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matlab编写的五个小波消噪程序，可供初学者仿真学习．(Matlab prepared by the five wavelet denoising process simulation study for beginners.)

APF SVG DC-DC 充电机 储能系统 微网发电 逆变器 均可以采用该套代码，实现目前电力电子产品市场的主流产品。特别适合正在研发电力电子产品的研发人员。(APF SVG DC-DC charging generator generation; electric power generation; generate electricity; send a telegram nverter; dc-to-ac converter; dc-to-ac inverter code)

实现对偶频带法生成宽间隔跳频序列，该程序为一个实现函数(To achieve dual-band method to generate wide interval frequency hopping sequence, the realization of the program as a function)

matlab编写的水准网平差程序，由测量数据可计算水准网平差结果，得到不错的水准网数据(levelling network computation by use of surveying datas and TSL application)

CS重构算法OMP算法。matlab源代码，。(CS reconstruction algorithm OMP)

Matlab潮流程序非常好用的程式 欢迎大家来使用(The trend of the procedure is very easy to use Matlab programs welcome you to use)

lvq and neural network

Function uses Standard Hough Transform to detect circles in a binary image. According to the Hough Transform for circles, each pixel in image space corresponds to a circle in Hough space and vise versa. Upper left corner of image is the origin of coordinate system.

Basics of wavelet function

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. )