tsp_ga

费希尔线性判别分析代码 Find the Fisher linear separator w (a column vector). X is is the training set (X is a matrix. Each row of X is a vector containing the features of a single sample). y is a column vector with the labels of the training set (1 and -1). alg is a number between 1 and 3 that specifies how to find w: alg = 1: w is the generalized eigenvalue of A,B. alg = 2: w is the solution to Bw = (mu_1-mu_2) alg = 3: solve the corresponding regression problem( Find the Fisher linear separator w (a column vector). X is is the training set (X is a matrix. Each row of X is a vector containing the features of a single sample). y is a column vector with the labels of the training set (1 and-1). alg is a number between 1 and 3 that specifies how to find w: alg = 1: w is the generalized eigenvalue of A,B. alg = 2: w is the solution to Bw = (mu_1-mu_2) alg = 3: solve the corresponding regression problem)

Description of godounov function in matlab jacobii function calcul numerique

To compute velocity and displacement from Acceleration data In put : Acceleration data and sampling rate in time (delta time) Out put : time. acceleration, velocity, and displacement.

基于细化算法的骨架提取算法，内涵测试图像，效果较好(Refinement algorithm based on the frame extraction algorithm,the effects is well)

防锁死刹车系统和电动液压助力转向系统的模型，模型什么详细(Anti-lock brake system and electro-hydraulic power steering system model, the model detailed)

MARKOv的应用 Step 1，求出各食品数据的增长率； Step 2，编程，使快速上升（>=0.03） ，在程序中用“2”表示；缓慢上升 (0,0.03)，在程序中用“1”表示；相对不变 0，在程序中用“0”表示；缓慢下降(-0.03,0) ，在程序中用“-1”表示；快速下降 (<=-0.03)，在程序中用“-2”表示；得到各类食品在不同时期的 ； Step 3，统计相邻增长率变化的一致的次数 ，就菜籽油而言，在2010-3-5到2011-3-15，其增长率为先快速增长后相对不变出现的次数为‘20’=1；具体42中食品的输出情况见附录； Step 4，对每一种食品，分五类进行叠加求和，分别为“先快速增长”、“先缓慢增长”、“先相对不变”、“先缓慢下降”、“先快速下降”；得到 ； Step 5，求出一步转移概率矩阵E ；即 每行的每五个数据依次与对应的 相除，得到概率矩阵E 的一行； Step 6，使用求出的一步转移概率矩阵E ，并求出不同时期的状态概率(MARKOv application Step 1, to obtain the growth rate of each food data Step 2, the programming, so fast rise (> = 0.03), in the program with " 2" slowly rising (0,0.03), used in the program " 1" indicates relatively constant 0, by the program " 0" indicates slow decline (-0.03,0), in the program by " -1" rapid decline (< =- 0.03), in the program with " -2" means get all kinds of food at different times Step 3, the same number of adjacent growth statistics change on rapeseed oil, in 2010-3-5 to 2011-3-15, Its growth rate is relatively rapid growth of the number of times after the first change occurs is ' 20' = 1 42 Output of specific food in Appendix Step 4, for each kind of food, five points are superimposed sum, respectively, " First of rapid growth," " first slow growth" , " first relatively unchanged" , " first slow down" , " first rapid decline" get Step 5, to obtain a step)

[mimi,i]=min(abs(imag(disp1)-imag(disp2))) legend( LHS of dispersion relation , RHS of dispersion equation ) xlabel( u,cl ) figure(1) clc uppercuts = input( Enter number of intersection points desired: ) [g,h] = ginput(uppercuts) w = sqrt(V.^2-u.^2) s = sprintf( Constants: ) disp(s) for p = 1:uppercuts s = sprintf( u( d d) = 0.10f ,L-1,p,u(p)) disp(s) s = sprintf( w( d d) = 0.10f ,L-1,p,w(p)) disp(s)

this is a small program that I use for some researches

A Practical Step by Step Speech Recognition System Good for both beginners and Experts. Codes are discuss too

基于matlab 视频流的车辆计数 可以直接使用 输入为。avi(You can directly use the input video stream based on the vehicle matlab count. avi)