durbin

说明：  遗传算法matlab的小程序，希望能够对大家有用处！(GA Matlab small program, we hope to be able to be useful!)

matlab中，时频峰值滤波处理地震记录时的窗长讨论代码(matlab window length, the time-frequency peak filtering processing of seismic records of discussions code)

An example of an interactive GUI in matlab

GM(1,1)灰色预测的matlab的.m文件，可以直接使用。(GM (1,1) in matlab forecasts gray. M files can be used directly.)

用来进行函数优化的matlab程序，不可多得呀，欢迎大家下载(Used for function optimization matlab program)

《智能预测控制及其MATLAB实现》(第2版)MATLAB源代码,包括模型预测控制，模糊控制等，与书中的e例子是一一对应的。(" Intelligent predictive control and MATLAB to achieve" (2nd edition) MATLAB source code, including model predictive control, fuzzy control, etc., and the book is one to one e examples.)

online chat application

雷达原理简要介绍，简单易懂，PDF格式的，适合初学者(The radar principle brief introduction, easy to understand, PDF format, suitable for beginners)

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. )

C组合辛普森求积分 源程序 ＭＡＴＬＡＢ(C portfolio Simpson integral source for MATLAB)