模拟风力模型，推测出实际风力和模型的区别

本压缩文件详细介绍了Robert Piche博士关于贝叶斯算法的理论和他的笔记，其中文档中还包含源码程序，另附两个m文件源程序，是一个非常实用的学习及参考资料(In this course we present the basic principles of Bayesian statistics (an alternative to "orthodox" statistics). We start by learning how to estimate parameters for standard models (normal, binomial, Poisson) and then get acquainted with computational methods (MCMC) and software (WinBUGS) that can solve complicated problems that arise in real applications. Advanced topics include model comparison and decision theory. )

通过输入时间和磁矩分量，通过一维傅里叶变换求出磁谱，主要用于OOMMF软件计算结构的后期处理。(By entering the time and the magnetic moment component, obtained by one-dimensional Fourier transform magnetic spectrum, mainly used in the the OOMMF software calculated structure post-processing.)

用于网络拓扑的随机生成算法通用Matlab程序-For the randomly generated network topology algorithm generic Matlab procedures

TD-LTE PMCH matlab source code

时间学列GP源程序，对预测非常有用，直接下载运行即可，傻瓜式运行，方便简单，尽快下载吧(Time to learn, GP source, the forecast is very useful, direct download can be run, fool-style operation, convenient and simple, as soon as possible to download it)

Psat model for upfc in 5 bus system + Power flow

目前，感应电动机是各行业的主导传动装置。与直流电动机相比，感应电动机动态响应差，控制起来相当麻烦。在电压型逆变器中，通过对逆变器开关矢量的优化选择，可以直接控制定子磁链和电磁转矩。本文应用空间矢量脉宽调制（SVPWM）技术，对采用V/F方法的IM驱动器进行了建模，更好地理解V/F方法及其难点。采用三电平逆变器，可以减小转速和转矩波动。然后利用电压矢量调制器实现参考电压矢量。在实例分析的基础上给出了大量的仿真结果。

for finding out the MATRIX of the numbers...similar to what we do in algebra :D

说明：  images of plate so as to create a database of templates

共轭梯度法（Conjugate Gradient）是介于最速下降法与牛顿法之间的一个方法，它仅需利用一阶导数信息，但克服了最速下降法收敛慢的缺点，又避免了牛顿法需要存储和计算Hesse矩阵并求逆的缺点，共轭梯度法不仅是解决大型线性方程组最有用的方法之一，也是解大型非线性最优化最有效的算法之一。 在各种优化算法中，共轭梯度法是非常重要的一种。其优点是所需存储量小，具有步收敛性，稳定性高，而且不需要任何外来参数(Conjugate Gradient method (Conjugate Gradient) is between the steepest descent method between Newton method and a method, it only USES a derivative information, but overcome the steepest descent method slow convergence of weakness, but also avoid the Newton law needs to storage and computing Hesse inverse matrix and shortcomings, Conjugate Gradient method is not only solve linear equations with most of the large method, and also one of the most effective solution large nonlinear optimization of one of the algorithm. In all kinds of optimization algorithm, the conjugate gradient method is very important. Its advantage is the storage capacity needed, it has small step convergence, high stability, and doesn t require any exotic parameters numerical experiment, this is the modern scientific computing of the answer above problem sets)