Writing_Fast_MATLAB_Code

matlab基础教程,从最简单的matlab程序教起(matlab basic tutorial, taught from the most simple matlab program)

在Matlab中，实现牛顿插值算法的源程序(In Matlab, the realization of Newton interpolation algorithm source)

This exchange file creates a clickable image map for a figure with links to multiple data sources

计算光子晶体能带结构图的MATLAB源码文件，使用的方法为平面波展开。各物理参数均有注释，光子晶体结构可任意调整。(Calculation of photonic band structure of the MATLAB source code files, the methods used for the plane wave expansion. The physical parameters have comments, photonic crystal structure can be adjusted.)

说明：  扩展卡尔曼滤波算法是目前最常用的非线性滤波算法之一，其核心思想就是将非线性模型线性化后，然后采用标准的卡尔曼滤波算法进行迭代滤波，这是一种最基本的解决非线性滤波问题的方法，目前在目标跟踪领域得到了广泛的运用。(Extended Kalman filter is the most commonly used one of nonlinear filtering algorithm, the core idea is that the nonlinear model is linearized, and then apply the standard Kalman filtering algorithm to filter, which is one of the most solve the basic problem of nonlinear filtering methods, the current target tracking has been widely used.)

考虑章动、极移、岁差下历元时刻月球在瞬时平天球坐标系，J2000协议地固系，WGS-84坐标系下的位置速度矢量及转换的matlab源程序(Consider nutation, polar motion, precession next epoch in the instantaneous level of the moon celestial coordinate system, J2000 protocol to the solid line, the position and the velocity vector conversion WGS-84 coordinate system matlab source)

matlab界面编程用来进行图像二值化，模糊聚类等操作(matlab GUI)

Press Numerica Methods in Engineering with MATLAB 2005

simulation de la turbine pur utilisation sur une eolienne

MATLAB函数参考手册，查看matlab函数作用以及功能。( SVMLSPex02.m Two Dimension SVM Problem, Two Class and Separable Situation Difference with SVMLSPex01.m: Take the Largrange Function (16)as object function insteads ||W||, so it need more time than SVMLSex01.m Method from Christopher J. C. Burges: "A Tutorial on Support Vector Machines for Pattern Recognition", page 9 Objective: min "f(A)=-sum(ai)+sum[sum(ai*yi*xi*aj*yj*xj)]/2" ,function (16) Subject to: sum{ai*yi}=0 ,function (15) and ai>=0 for any i, the particular set of constraints C2 (page 9, line14). The optimizing variables is "Lagrange Multipliers": A=[a1,a2,...,am],m is the number of total samples.)