UDPMultiCast

计算机仿真程序例子，包含多个基本程序，可帮助我们更好的学习matlab(examples of computer simulation program, includes a number of basic procedures that will help us to better learning Matlab)

对啁啾和色散概念的理解，通过MATLAB编程实现可视化(matlab code for chirp and dispersion)

以Simulink为主要工具介绍了系统仿真方法与技巧，包括连续系统、离散系统、随机输入系统和复数系统的仿真。介绍了模声封装技术、电力系统模块集、非线性系统设计模块集、S-函数编写与应用、Stateflow有限状态机、虚拟现实工具箱等中高级使用方法，最后还介绍了半实物仿真技术与实时控制技术。(Simulink as the main tool to introduce a system simulation methods and techniques, including continuous systems, discrete systems, stochastic input system and the simulation of complex systems. Introduce a sound module packaging technology, power system module set, non-linear system design blockset, S-function of the preparation and application, Stateflow finite state machine, virtual reality and other high use of the toolbox, and finally also introduce a semi-physical simulation technology and real-time control technology.)

extreme learning machine例子 run sinc_mean(extreme learning machine examples run sinc_mean)

基于去斜处理的线性调频雷达回波生成及压缩(Inclined to deal with based on the linear frequency modulated radar echo generation and compression)

Buck converter for power electronics

实现ITRF2008到ITRF2005,ITRF2000,ITRF97三种坐标框架的坐标转换(transform coordinate from ITRF2008 to ITRF2005,ITRF2000,ITRF97)

Character recognition

自适应动态规划（ADP）actor-critic框架用于最优控制的matlab仿真程序代码，实测可用(Adaptive dynamic programming (ADP) actor-critic matlab simulation framework for optimal control of the program code, found available)

说明：  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。(The Conjugate Gradient method is a method between the steepest descent method and the Newton method. It only needs to use the first derivative information, but overcomes the shortcomings of the steepest descent method and avoids the need for the Newton method to store And the calculation of Hesse matrix and the inverse of the shortcomings, conjugate gradient method is not only to solve large-scale linear equations one of the most useful methods, but also solution for large-scale nonlinear optimization of one of the most effective algorithm. In a variety of optimization algorithms, conjugate gradient method is a very important one. The advantage is that the required storage capacity is small, with step convergence, high stability, and does not require any external parameters.)