MMC_CPS_4LEVEL

可逆跳跃马尔科夫蒙特卡洛贝叶斯模型选择，主要用于神经网络(Reversible Jump MCMC Bayesian Model Selection This demo demonstrates 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.)

本程序是用五点差分格式解拉普拉斯方程，采用MATLAB作为开发环境，拉普拉斯方程有广泛的应用，五点差分格式的精度高 (This procedure is a five-point difference scheme for solution of Laplace equation, using MATLAB as a development environment, Laplace equation has a wide range of applications, a five-point difference schemes of high accuracy)

Iterative algorithm for minimum cross entropy thresholding

根据输入的本原多项式系数、级数、延迟相位产生一个m序列。。(creat a m sequence )

matlab分类工具箱源码，在matlab下运行可以对数据进行多种分类，并有说明文档，可以根据文档进行操作，实现分类。(classification Matlab toolbox source, Matlab can run on a variety of data classification, and documentation, according to documentation for operation, achieving classification.)

说明：  能实现无线传感器网络仿真中的算法要求的leach算法值得你拥有！！！！！！！！！！(leach algorithm)

说明：  无线充放电中的谐振网络仿真设计，有助于了解谐振网络。(The simulation design of resonant network in wireless charge and discharge is helpful to understand resonant network.)

robotics tool for matlab

本代码是基于matlab开发的用于图像处理中针对灰度图像进行的直线匹配检测(Detect lines in grayscale image by matlab)

经典谱分析和改进算法，用于估计信号功率谱和方位，(Classical spectral analysis and improved algorithms for estimating the signal power spectrum and the position,)