Componente-Backup-MySql

MATLAB循环相关，循环卷积函数的编写，使用几个函数实现。(MATLAB cycle related to the preparation of cyclic convolution function, the use of several function.)

我们地震处理相关的一些matlab程序，包括雷克子波，segy的读写，动校正这些(We are dealing with some of the earthquake procedures matlab procedures, including the mine sub-wave, segy read and write, dynamic correction of these)

说明：  联邦UKF的matlab资料，关于信息融合，希望大家借鉴。(Federal UKF the matlab data on information fusion, I hope you learn.)

说明：  使用一种方法来产生一组具有不同PSD的相关高斯过程的采样值(Use a method to generate a set of different Gaussian process of PSD-related samples)

给出了线性方程组的CG法、Gauss_Seidel、SOR、Jacobi、fem求解，(Linear equations given the CG method, Gauss_Seidel, SOR, Jacobi, fem solution)

说明：  太阳能光伏电池的matlab/simulink 仿真模型，通过编辑s函数实现，可以对光伏特性进行仿真(Solar photovoltaic cells matlab/simulink simulation model, by editing the s function implementation, you can simulate the characteristics of PV)

说明：  本程序实现多模态故障检测与故障诊断，用的方法是pca_GMM_FJ，对象为TE,GMM是用的FJ算法，效果很好(This procedure realize multimodal, fault detection and fault diagnosis method is pca_GMM_FJ, object for TE, GMM FJ algorithm is used, the effect is very good)

choudhury教授的关于阀门粘滞模型的Matlab/Simulink 模型，绝对真实，本人亲自运行成功。(A valve stiction model of professor Choudhury. )

三相电压源型逆变器的滞环跟踪PWM的simulin模型，我使用的是matlab2012a(current lagloop tracking PWM inverter)

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. )