dim_reduction

名称：带交叉因子的改进PSO算法功能：求解多维无约束优化问题特点：收敛性强，还可以加入变异算子(Name: Improved PSO algorithm with cross-factor: Solving multidimensional unconstrained optimization problem characteristics: strong convergence can also join the mutation operator)

读取Yokogawa DL 750, DL 1600 系列 与 DL 7440 示波器的数据. 特点：允许多个通道（trace）的输入，如： [x(:,1:4),x(:,5)]=wvfreadb_moreinput(filename, 1, [1,2,3,4], 1) (loads YT-waveform data from*.wvf file saved by Yokogawa DL 750, DL 1600 series and DL 7440 Oscilloscopes into the variables y (y data) and t (time data). The matrices ind_over and ind_under, illegal contain the indices of overranged data points outside the upper/lower limit of the TDS AD converter and indices of illegal values. advantage：permit several traces, e.g. [x(:,1:4),x(:,5)]=wvfreadb_moreinput(filename, 1, [1,2,3,4], 1) )

Salesman Problem - Genetic Algorithm matlab code

说明：  matlab辅助小波分析与应用的源码，包含了一些常用的小波分析常规算法和快速算法，能有效帮助学习小波分析(Matlab support wavelet analysis and application of the source code contains a number of commonly used conventional wavelet analysis algorithm and fast algorithm, can help learning wavelet analysis)

Canny Edge Detector In Matlab

Beta Gamma Nakagami Normal Rayleigh Rician Weibull

这是一个经典的形变模型实施,在一个单一的文件用简单的可以理解的代码。 功能包括两部分一个简单的弱分类器和一个促进部分: 弱分类器试图找到最佳阈值的数据维数对数据进行分离成两个阶级1和1 要求的进一步提高分类器部分迭代,每一步是变化分类权重miss-classified例子。这造成了一连串的“弱分类器”,表现得像一个“强大分类器” (This a classic AdaBoost implementation, in one single file with easy understandable code. The function consist of two parts a simple weak classifier and a boosting part: The weak classifier tries to find the best threshold in one of the data dimensions to separate the data into two classes-1 and 1 The boosting part calls the classifier iteratively, after every classification step it changes the weights of miss-classified examples. This creates a cascade of "weak classifiers" which behaves like a "strong classifier" . )

嵌入水印信息的MATLAB程序,是转载别人的，如有侵权，马上删除(MATLAB program embedded watermark is reproduced someone else' s, if infringement, immediately remove)

syms x g=exp(x*sin(x)) t=taylor(g,12,2) xd = 1:0.05:3 yd = subs(g,x,xd) ezplot(t, [1,3]) hold on plot(xd, yd, r-. ) title( Taylor approximation vs. actual function ) legend( Taylor , Function )(syms x g = exp (x* sin (x)) t = taylor (g, 12,2) xd = 1:0.05:3 yd = subs (g, x, xd) ezplot (t, [1 , 3]) hold on plot (xd, yd, ' r-.' ) title (' Taylor approximation vs. actual function' ) legend (' Taylor' , ' Function' ))

运用matlab模拟各种随机变量，内容包括模拟的基本思路和matlab代码(Use matlab simulate various random variables, including the basic ideas and matlab simulation code)