3machine-transient-stability

用数值方法实现该fsk信号的相关型解调器，完成一个二进制fsk通信系统的仿真(Numerical methods used in the implementation of the relevant type fsk signal demodulator, to complete a binary fsk communication system simulation)

用直接解得办法求摄影测量中的相对定向元素( use direct measurement to find the relative orientation elements in photography)

一个使用matlab实现进化博弈过程仿真实验的例子(This function uses ES to optimize a function f(x_1,...,x_n). There are a set of 55 test functions to check the effectiveness of the algorithm when optimizing functions. Feel free to leave comments, corrections or suggestions. Read the comments in the code for more information and bibliography used in the development of this code.)

Transmission line control example created in MATLAB Simulink enviroment , This model illustrate the exact modelling of transmission lines in real scenarios.

弹性波二维模拟，PLM边界吸收和clayton边界吸收的比较（c++和matlab）(Two-dimensional simulation of elastic waves, PLM boundary absorption and clayton boundary on the absorption of (c++ and matlab))

最速下降法 优化算法问题以及其说明等等 (decent answerr method )

此程序为解线性方程组的Gauss-Serdel迭代法的源程序(This procedure for solving linear equations Gauss-Serdel source iteration)

Rayleigh信道的仿真及图。可直接运行产生形象的图形结果。用于各种算法前期的信道仿真。(Rayleigh channel simulation and Figure. The image can be run directly generate graphical results. Various algorithms for the pre-channel simulation.)

本资料是一篇很好的文章，希望对大家有帮助(This material is a very good article，We want to help)

This tutorial is designed to give the reader an understanding of Principal Components Analysis (PCA). PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension. Before getting to a description of PCA, this tutorial first introduces mathematical concepts that will be used in PCA. It covers standard deviation, covariance, eigenvectors and eigenvalues. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.