simulink

很好用的投影寻踪程序 matlab开发 (Well with the development of PPE program matlab)

lorenzattractor. all the equations

基带传输二电平码的simulink模拟产生程序(Simulink analog baseband transmission two-level code generator)

小波包分析提取振动信号中的特征频率，时间序列数据分析中的梅林变换工具，课程设计时编写的matlab程序代码。( Wavelet packet analysis to extract vibration signal characteristic frequency, Time series data analysis Mellin transform tool, Course designed to prepare the matlab program code.)

说明：  一个模糊神经网络的参数训练程序，希望对大家有所帮助(The parameters of a fuzzy neural network training procedures, we want to help)

就是主要产生高斯白噪声的相关文件吧 这个文档主要介绍的是高斯白噪声的相关资料(This document describes the relevant information Gaussian white noise)

中心逼近时的灰色预测，根据实际情况通过不断调整matlab程序中的m值，达到更高的精度。(Change the grey prediction of background value, through the continuous adjustment of the m value in the matlab program according to the actual situation, to achieve higher accuracy.)

matlab 和 simulink教程，很适用的(MATLAB_and_simulink_tutorial)

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. )

通信中位同步的gardner算法的matlab仿真(Communications Synchronization gardner of the Matlab simulation algorithm)