matlab_tool_box_spider

无向图求最短路的floyd算法通用matlab程序 (Undirected graph for the shortest path algorithm floyd General matlab program )

OFDM原理国外解释详细文档。及matlab实现。有时域、频域、相位、功率谱等图参照。(OFDM Principles of documents abroad, a more detailed explanation. And matlab implementation. Sometimes domain, frequency domain, phase, power spectra map reference.)

说明：  基于matlab的编程，信道仿真的，很好很强大(Matlab-based programming, channel simulation, very good very strong)

闭环系统的奇异值曲线仿真和其鲁棒性能分析(Singular value of the closed-loop system curve simulation and robust performance analysis)

基于贝叶斯理论，针对五层框架结构，利用模型更新的原理对结构物理参数进行识别(Based on the Bayesian theory, the structural physical parameters are identified by the principle of model updating for the five-layer frame structure)

基础卡尔曼滤波。附有滤波初始条件，适合初学者(Basic Kalman filter. With filter initial conditions, suitable for beginners)

sismic work for the class of geophysics, This code was made for my coworkers

利用MATALB编写的潮流程序，先是编写总体框架，然后每一部分作为一个子程序。(MATALB prepared using the flow program, first write the overall framework, and then each part as a subroutine.)

Classification of an LTI system by type of damping and design solution of a system of first and second order, entering the transfer function or system parameters

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.)