droop

matlab course band pass high pass low pass butterworth fir iir imuplse response

拉丁超立方抽样，调用方式如下：S=lhs(m,dist,mu,sigma,lowb,upb) m: a scalar,the number of sample points dist: A row with distribution type flags of basic random variables the value of the flag can be 1 (for uniform distribution, 2(for normal distribution), 3(for lognormal) and 4(for extreme type 1). mu: A row vector comprising the mean value of basic random variables. sigma: A row vector with its length equaligng to mu,including the standard deviation of basic random variables. lowb: a row vector with its elements are the lower bound of the sampling interval upb:a row vector with its elements are the upper bounds of the sampling interval dist,mu,sigma,lowb,upb must have the same length. Output argument S: sampling point matrix, of which each row is a sampling point.(code of Latin Hypercube Sampling)

用遗传算法解决最短路径优化TSP问题(附matlab源程序.txt)(The solvelution of the shortest path optimization problem TSP by using genetic algorithms)

rotor reference frame model of induction motor

说明：  基于Matlab的计算方法中常用的算法程序，包括高斯消去等(Based on the calculation of Matlab algorithms commonly used in procedures, including Gaussian elimination, etc.)

采用有限单元法计算平面刚架自振频率，可生成单刚、单元质量阵并组成总刚、总质量矩阵，并绘图显示。(Plane frame using the finite element method to calculate the natural frequency can be generated just a single unit consisting of the total mass matrix and just total mass matrix, and graphics.)

高阶谱分析，很实用，能帮助分析非平稳信号(Higher order spectral analysis, it is useful to help analyze non-stationary signals)

基于GA优化的SVM算法，它可以被用于预测分类(SVM algorithm based on GA optimization for prediction classification)

说明：  Matlab细化算法，重要参考资料，细化效果较好。(Matlab thinning algorithm, an important reference materials, refinement is better.)

利用MATLAB制作统计报告或报表；从文件中读取数据到MATLAB；从MATLAB中导出数据到文件；数据的平滑处理、标准化变换和极差归一化变换；生成一元和多元分布随机数；蒙特卡洛方法；参数估计与假设检验；Copula理论及应用实例；方差分析；基于回归分析的数据拟合；聚类分析；判别分析；主成分分析；因子分析；图像处理中的统计应用等。 (Making statistical reports or reports by MATLAB to read data from the file to the MATLAB derived from the data to a file in MATLAB data smooth processing, standardization transformation and range normalization transformation generation of univariate and multivariate distribution random number Monte Carlo method parameter estimation and hypothesis testing Copula theory and examples of application analysis of variance data fitting based on regression analysis cluster analysis discriminant analysis principal components analysis factor analysis image processing in the application of statistics.)