suipan

multivariate analysis tool

This document contains a tutorial on Matlab with a principal components analysis for a set of face images as the theme

MATLAB 编程（第二版） 《MATLAB Programming for Engineers》的中文版，最好的编程教材。 (MATLAB Programming for Engineers （Second Edition） )

用于matlab 的学习，前五章内容为基本介绍，包括软件安装与卸载，数据类型，数组与matalab常用功能(For the matlab learning, basic introduction to the first five chapters content, including software installation and uninstallation, data types, arrays with common functions matalab)

带免疫功能的粒子群算法，在matlab环境实现。(With immune function of particle swarm optimization, in the matlab environment realize.)

步距电机控制代码。可以在X，Y，Z三个方向移动电机。(This code is written to control step motor, which is important in some measurments where precision is critical.)

卡尔曼滤波器算法的实现，通过对信号的追踪，描绘运动质点的轨迹，最后对算法优劣进行了误差分析，效果验证良好。(kalman filter)

Smooths a matrix (with/without NaN s) via recursive moving average method and eliminates data gaps. (MOVING_AVERAGE(X,F) smooths the vector data X with a boxcar window of size 2F+1, i.e., by means of averaging each element with the F elements at his right and the F elements at his left. The extreme elements are also averaged but with less data, obviously. Leaving the edges intact. The method is really fast. MOVING_AVERAGE2(X,M,N) smooths the matrix X with a boxcar window of size (2M+1)x(2N+1), i.e., by means of averaging each element with its surrounding elements that fits in the mentioned box centered on it. This one is also really fast. The elements at the edges are averaged too, but the corners are left intact. NANMOVING_AVERAGE(X,F) or NANMOVING_AVERAGE(X,F,1) accept NaN s elements in the vector X the latter interpolates also those NaN s elements surrounded by numeric elements. NANMOVING_AVERAGE2(X,M,N) or NANMOVING_AVERAGE2(X,M,N,1) accept elements NaN s in the matrix X the latter interpolates also those NaN elements surrounded by numeric elements.)

用遗传算法求解自变量是多维的函数的最优值(Genetic Algorithm for the optimal value of Multi-dimensional function)

本程序描述了pca在人脸识别上的应用。并带有输入输出，可在人脸库上进行试验。(This procedure describes the pca in face recognition applications. And with the input and output can be tested on the face database.)