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MATLAB常用算法程序集，功能比较齐全。(MATLAB commonly used algorithm for assembly, function relatively complete.)

风力双馈发电机运行的仿真模型，该模型和文献给出聊定子磁链定向控制，仿真效果很棒！(Double-fed wind generator to run the simulation model, the model given in the literature and talk to the stator flux oriented control, simulation results were fantastic!)

说明：  用matlab实现的K2算法，需要用到贝叶斯网络工具箱(K2 algorithm implemented with matlab)

用时间序列分析中的自适应滤波预测法,建立某市用电量的预测模型 (Using time series analysis of adaptive filtering prediction method, a city electricity consumption forecasting model)

MPC for MIMO SYSTEM

基于小波变换模极大的多尺度图像边缘检测matlab源代码(Based on wavelet transform modulus maxima of multi-scale edge detection matlab source code)

用matlab编写的经典算法程序——解微分方程，供大家参考(Classical algorithm using matlab program- solving differential equations, for your reference)

基于 Matlab 的积分分离 PID 控制 算法及仿真 摘 要 MATLAB 是一种集数值计算、符号运算、可视化(flying car)

一个简单的调用matlab DWT函数进行离散小波变换的示例程序(A simple function call matlab DWT DWT sample program)

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)