EULER_forward_ODE

随着用户数的增加，线性多用户检测器的误比特率的比较。(With the number of users increases, the linear multi-user detector bit error rate comparison.)

matlab教程，线性代数部分，很全很有用，特别是对于解线性方程组，赶快收藏啊！(matlab tutorial, linear algebra part, it is all very useful, especially for solving linear equations, hurry collection ah!)

MATLAB相关接口介绍的一本很实用的书，非常适合初学者(MATLAB interface)

功率谱估计的各种方法，包括经典法和现代法的matlab实现，不是通过调用函数，而是根据算法的思想实现的，希望对大家有用。(The power spectrum estimation methods, including the classic method and modern method of Matlab implementation, not by calling the function, but according to the idea of ​ ​ the algorithm to achieve, and the hope that useful.)

smarty+中文手册smarty+中文手册smarty+中文手册smarty+中文手册(smarty+ English Manual smarty+ English Manual smarty+ English Manual smarty+ English Manual)

用matlab编程做的《有限元法基础与程序设计》第二章的例子 方法三(FEA matlab )

matlab对图形处理车牌倾斜校正实现的仿真实现等等(matlab correct license plate)

MATLAB IMPLEMENTATION

torbo encoder codeing

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. )