mmplayer

DOA估计的经典算法esprit算法的求根算法，在matlab中能够完全仿真。(DOA estimate of the classical esprit of the algorithm is extract roots algorithm, in matlab simulation can completely.)

此算法功能强大，可以解决好多线性规划问题，同时解决许多，简单易懂，好用(This algorithm is powerful, can solve a lot of linear programming problems, while addressing the many, easy to understand, easy to use)

T_XTIDE Tidal prediction YOUT=T_XTIDE(STATION) makes a tidal prediction for the current day using the harmonics file XTIDE. Predictions at other times can be generated using [...]=T_XTIDE(STATION,TIM) where TIM is a vector of matlab-format decimal days ( DATENUM). If TIM is a scalar then it is taken as the number of days present over which to make predictions. If TIM>1000 then it is taken to be the starting time in matlab-format. at the matlab time JD (UTC, but see code for details) where tau = lunar time s = mean longitude of the moon h = mean longitude of the sun p = mean longitude of the lunar perigee np = negative of the longitude of the mean ascending node pp = mean longitude of the perihelion (solar perigee)

说明：  一个图形界面，可以输入不同类别的信号进行仿真(PLOT )

this is the matlab simulation for BER exaction

反步法求取船舶的运行轨迹，是大连海事大学某学生毕业论文中的仿真图形。(Backstepping strike a ship running track, Dalian Maritime University, a student thesis in the simulation graphics.)

轨道根数与状态向量的转换，已知状态向量求解轨道根数！(Orbital and state vector conversionSolving the state vector is known orbital elements ! 。 )

本程序是矩形域高斯二重积分，利用matlab编写而成(This procedure is a double integral Gaussian rectangular domain, using matlab prepared)

说明：  有关拉格朗日和牛顿插值的算法描述及MATLAB编程实现(The Lagrange and Newton interpolation algorithm description and MATLAB programming)

K最邻近密度估计技术是一种分类方法，不是聚类方法。 不是最优方法，实践中比较流行。 通俗但不一定易懂的规则是： 1.计算待分类数据和不同类中每一个数据的距离（欧氏或马氏）。 2.选出最小的前K数据个距离，这里用到选择排序法。 3.对比这前K个距离，找出K个数据中包含最多的是那个类的数据，即为待分类数据所在的类。(K nearest neighbor density estimation is a classification method, not a clustering method. It is not the best method, but it is popular in practice. Popular but not necessarily understandable rule is: 1. calculate the distance between the data to be classified and the data in each other (Euclidean or Markov). 2. select the minimum distance from the previous K data, where the choice sorting method is used. 3. compare the previous K distances to find out which K data contains the most data of that class, that is, the class to which the data to be classified is located.)