zabo

说明：  用MATLAB进行时间序列分析,聚类分析和启发式算法一些介绍。个人收集，很有价值(Using MATLAB to carry out time series analysis, clustering analysis and heuristic introduction. Personal collection, great value)

matlab程序，里面有关于数学建模的各种程序，很实用(matlab program, there are various programs on mathematical modeling, it is practical)

lda基本思想是选择使得Fisher准则函数达到极值的向量作为最佳投影方向，从而使得样本在该方向上投影后，达到最大的类间离散度和最小的类内离散度(Linear classification algorithm)

EWB DC-DC CONVERTER PROJECT

matlab6.5参考手册，是英文版的。里面含有simulink的参考手册。(matlab6.5 Reference Manual is in English. Simulink which contains a reference manual.)

基于 matlab 的 DSP 时间管理器(The DSP-based time manager matlab)

七单元天线阵MUSIC DOA估计： d=1 ， 天线阵元的间距； lma=2， 信号中心波长； 四输入信号； A=[A1,A2,A3,A4]， 得出A矩； 四信号的频率d=[1.3*cos(v1*n) 1*sin(v2*n) 1*sin(v3*n) 1*sin(v4*n)] 构造输入信号矢量 U=A*d 总的输入信号 总输入信号的协方差矩阵 [s,h]=eig(c) 求协方差的特征矢量及特征值 取出与零特征值对应的特征矢量 求协方差矩阵的逆矩阵 应用Music法估计输出 绘出各波达方向图(Seven-element antenna array MUSIC DOA estimates: d = 1, Antenna Array pitch LMA = 2 signal center wavelength four input signals A = [A1, A2, A3, and A4], drawn A moment tetra-frequency of the signal D = [1.3* cos (V1* n) 1* ⁢ sin (v2* n) 1* sin (v3* n) 1* sin (V4* n)] constructed input signal vector U = A* D of the total input signal of the total input signal covariance matrix [S] = EIG (c) seeking covariance feature vector and the feature value removing and corresponding to the zero eigenvalues ​ ​ characterized vector seeking covariance matrix inverse matrix Applications Music estimate output plotted DOA Figure)

simple Monte Carlo tutorial

利用matlab的gui设计的带有输入和输出功能的界面图形，实现等距线可视性很强，自己独立编写的数据和图形，学习非常值得,实现pca svm压缩图像，matlab语言(Using GUI of MATLAB to design interface graphics with input and output functions, realizing the visibility of isometric line is very strong, the data and graphics written independently by oneself are very worthwhile to learn, and realizing PCA SVM compressed image, MATLAB language)

说明：  比较三次样条插值和拉格朗日插值多项式对runge函数插值的效果并作图解释(Comparison of cubic spline interpolation and Lagrange interpolation polynomial interpolation of the Runge function and mapping to explain the effect of)