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Dennis M.Sullivan FDTD书后matlab程序 已经调试验证 第三章(Dennis M.Sullivan matlab program using FDTD book have been debug and verify)

PLS GIVE MATLAB CODING .......

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. )

Brown_Acoustic_Simulator Matlab source code for acoustic simulation

CVaR的计算，以及cvar最优化的投资组合权重问题(VAR CVAR OPTIMAL)

灰色预测 可以在数学建模中使用 可以运行。 (Grey prediction)

基于matlab GUI的一个简单计算机系统，能实现加减乘除等简单算法(A simple matlab GUI based computer system, to achieve a simple algorithm for addition, subtraction, etc.)

This is a fast non-iterative ellipse fit, and among fast non-iterative ellipse fits this is the most accurate and robust. It takes the xy-coordinates of data points, and returns the coefficients of the equation of the ellipse: ax^2 + bxy + cy^2 + dx + ey + f = 0, i.e. it returns the vector A=(a,b,c,d,e,f). To convert this vector to the geometric parameters (This is a fast non-iterative ellipse fit, and among fast non-iterative ellipse fits this is the most accurate and robust. It takes the xy-coordinates of data points, and returns the coefficients of the equation of the ellipse: ax^2+ bxy+ cy^2+ dx+ ey+ f = 0, i.e. it returns the vector A=(a,b,c,d,e,f). To convert this vector to the geometric parameters )

使用MZM实现QPSK，内含QPSK的星座图，输入输出图像，输出相位变化图，眼图(Achieve QPSK using MZM , containing QPSK constellation, input and output images, the phase diagram of output, eye diagram)

说明：  牛顿下山法标准程序，数值计算研究必备，通俗易懂(newton downhill algorithm)