PCA

matlab的命令文档，可以很方便地查询matlab的命令，帮助我们编写程序(Matlab ordered document can be easily inquires the matlab orders, help us write a program)

梯度下降法是最早最简单，也是最为常用的最优化方法。梯度下降法实现简单，当目标函数是凸函数时，梯度下降法的解是全局解。一般情况下，其解不保证是全局最优解，梯度下降法的速度也未必是最快的。梯度下降法的优化思想是用当前位置负梯度方向作为搜索方向，因为该方向为当前位置的最快下降方向，所以也被称为是”最速下降法“。最速下降法越接近目标值，步长越小，前进越慢。(The gradient descent method is the earliest and most simple and most commonly used optimization method. The gradient descent method is simple to realize. When the objective function is a convex function, the solution of the gradient descent method is a global solution. In general, the solution is not guaranteed to be the global optimal solution, and the gradient descent method is not necessarily the fastest. The optimization idea of gradient descent method is to use the current position negative gradient direction as the search direction, because the direction is the fastest descending direction of the current position, so it is also called the steepest descent method. The faster the slowest descent approach is closer to the target, the smaller the step, the slower the progress.)

matlbs file for lyapanof

导线网平差，主要是附和导线平差程序，用于计算各点坐标并评定其精度。(Wire network adjustment, mainly echoing the wire adjustment procedure used to calculate the coordinates of each point and to assess its accuracy.)

This is my first upload which contain some stuff files from my final project.

Underwater wireless sensor network

PERFORMANCE EVALUATION OF THREEPHASE INDUCTION MOTOR DRIVE FED FROM Z-SOURCE INVERTER

基于平稳-非平稳的滤波技术，主要应用于信号滤波，去噪。(Based on the stationary- non-stationary filtering technology, mainly used in signal filtering, de-noising.)

这是MATLAB学习资料，其中有很多学习相关例子，望对大家能有用(MATLAB learning materials, there are a lot of study relevant examples, we can look to useful)

Li near r egr essi on, acti ve learning We arriv ed at the lo gistic regression model when trying to explicitly model the uncertainty about the lab els in a linear c la ss ifier. The same genera l modeling approach p e rmits us to use line ar predictio ns in var ious other co ntexts as well. The simplest of them is regress ion where the go al is to pr e dict a con tin uous resp onse y t ∈ R to e ach example ve ctor. Here to o fo cusing on linear predictions won’t b e inherently limiting as linear predictions can b e easily extended (ne xt lecture).