doa_music1

计算电磁学 三维PML吸收边界程序 采用MATLAB编写(Three-dimensional computational electromagnetics PML absorbing boundary program written using MATLAB)

QPSK通信系统仿真，有所有的代码，课程设计或者实验可以参考(QPSK communication system simulation, there is all the code, curriculum design or experiment can refer)

说明：  有关于matlab 编程 人脸检测等等，关于adaboost级联分类器(About matlab programming)

说明：  利用动态规划算法求解最优路径，混合动力卡车、扭矩分配，SOC估计(Using dynamic programming algorithm to solve the optimal path)

多传感器目标交叉定位算法(垂线法),内含两个、三个、四个传感器交叉定位程序。(Multisensor target cross localization algorithm (vertical method), containing two, three, four sensors cross localization process. )

An program for transmission line is given in this file.For Short the length is 80 km,for medium 80 to 250 km and for long transmission length is given by 250 km above.

说明：  计算连续方程Lyapunov指数的程序,自己编写的，比较好用(consecutive terms of Lyapunov exponent equation procedures, the preparation of their own, more user friendly)

预测控制里面MAC算法的实现，简单明了，欢迎交流(programming for prediction control)

利用VC和Matlab混合编程,基于Matcom，Release文件夹下可直接运行，绘出震荡曲线(Using VC and Matlab to program, based on Matcom, Release folder can be run directly and shock curve can be plotted.)

遗传算法GA—模拟自然界遗传机制和生物进化论而成的一种并行随机搜索最优化方法。（具有“生存+检测”的迭代过程的搜索算法）基于自然界“优胜劣汰，适者生存”的生物进化原理引入优化参数形成的编码串联群体中，按照所选择的适应度函数并通过遗传中的选择、交叉和变异对个体进行筛选，使适应度值好的个体被保留，适应度差的个体被淘汰，新的群体既继承了上一代的信息，又优于上一代。反复循环，直至满足条件。(GA-A parallel random search optimization method that simulates natural genetic mechanisms and biological evolution. (Search algorithm with "survival + detection" iterative process) Based on the biological evolution principle of "survival of the fittest, survival of the fittest" in the natural world, the coding series formed by optimized parameters is introduced. Individuals are screened according to the selected fitness function and through genetic selection, crossover, and variation, so that individuals with good fitness values are retained, individuals with poor adaptability are eliminated, and new groups inherit the information of the previous generation., Better than the last generation. Repeat until conditions are met.)