基于前景理论三参数区间灰数

用Hanning窗设计线性相位滤波器，绘制幅频和相位特性曲线，并在改变N的值后观察N对滤波器特性的影响及其时域特点。(Hanning window with a linear phase filter design, drawing amplitude frequency and phase characteristic curve, and change the value of N, N were observed on the filter characteristics and time domain characteristics.)

this code will help you in minimizing of in-line coded functions

实验一 Matlab使用及离散时间傅立叶变换 实验二应用傅立叶变换对信号进行频谱分析 实验三 数字滤波器的设计 (Experiments using Matlab and a discrete-time Fourier transform second experiment the signal Fourier transform spectrum analysis experiment three digital filter design )

说明 cloudPlot将有助于形象化的一个二维数据集的分布。这是特别有用当在非常大的数据集凡redular阴谋（的x，y, . )只是填充纯色的情节，因为测量点重叠看着对方。 cloudPlot使用内置的例程在MATLAB中轴线设置限制，并适当格点的数据。 MATLAB的释放MATLAB的7.7（R2008b） (Description cloudPlot will help visualize the distribution of a 2-dimensional dataset. It is especially helpful when looking at extremely large datasets where a redular plot(x,y, . ) will just fill the plot with a solid color because the measurement points overlap each other. cloudPlot uses the built-in matlab routines to set the axis limits and grid points appropriate to the data. MATLAB release MATLAB 7.7 (R2008b))

PSO with linkage operator

direct torque documentation

Basic lab experimental prblm for matlab

这是模式识别中最小错误率Bayes分类器设计方案。 自行完善了在不同先验概率的条件下，男、女错误率和总错误率的统计，放入各个数组当中。 全部程序由主函数、最大似然估计求取概率密度子函数、最小错误率贝叶斯分类器决策子函数三块组成。 调用最大似然估计求取概率密度子函数时，第一步获取样本数据，存储为矩阵；第二步对矩阵的每一行求和，并除以样本总数N，得到平均值向量；第三步是应用公式（3-43）采用矩阵运算和循环控制语句，求得协方差矩阵；第四步通过协方差矩阵求得方差和相关系数，从而得到概率密度函数。 调用最小错误率贝叶斯分类器决策子函数时，根据先验概率数组，通过比较概率大小判断一个体重身高二维向量代表的人是男是女。 主函数第一步打开“MAIL.TXT”和“FEMALE.TXT”文件，并调用最大似然估计求取概率密度子函数，对分类器进行训练。第二步打开“test2.txt”，调用最小错误率贝叶斯分类器决策子函数，然后再将数组中逐一与已知性别的数据比较，就可以得到不同先验概率条件下错误率的统计。 (This is the minimum error rate pattern recognition Bayes classifier design. Self- improvement prior probability in different conditions , male , female and total error rate error rate statistics , into which each array . All programs from the main function , maximum likelihood estimation subroutine strike probability density , the minimum error rate Bayesian classifier composed of decision-making three subfunctions . Strike called maximum likelihood estimate probability density subroutine , the first step to obtain the sample data , stored as a matrix the second step of the matrix, each row sum , and divided by the total number of samples N, be the average vector third step is to application of the formula ( 3-43 ) using matrix and loop control statements , obtain the covariance matrix fourth step through the variance-covariance matrix and correlation coefficient obtained , resulting in the probability density function . Call the minimum error rate decision Functions Bayesian)

Program for solving the steady one-dimensional convection/diffusion equation.

Iam uploaded the blog theam file