数字滤波器分解

MATLAB的图像处理功能，功能十分强大，内容也很丰富(MATLAB image processing functions, is very powerful, very rich in content)

Simulation of Rossler ODE

LMS三角定位迭代加权算法 LMS三角定位迭代加权算法 LMS三角定位迭代加权算法(LMS triangulation iterative weighting algorithm LMS triangulation iterative weighting algorithm LMS triangulation iterative weighting algorithm)

运用wind matlab 进行量化投资, 例子包括 期货 期权 股票 债券等 (some samples for quantitative investing using matlab)

频谱细化分析的程序，程序包括着逐渐完成的几个过程程序，注意频谱细化是针对开始对象时连续的。(Detailed analysis of the spectrum, the program includes a process of gradual completion of several procedures, attention to refine the spectrum when the object is for consecutive starts.)

ieee 9-bus system test network implemented in simulink matlab

图像索材，学习图像处理必须的一些常用图片，愿大家能轻轻松松学习图像！(Image cord material, learn to be a number of image processing used pictures, would like to learn that we can easily image!)

Direct torque control of IM

在MATLAB中使用决策论的方法进行调制方式识别，可以识别AM，FM,USB,LSB,ASK,PSK,FSK等多种调制方式(In MATLAB using the decision theory approach to modulation recognition, you can identify the AM, FM, USB, LSB, ASK, PSK, FSK modulation and other)

这是模式识别中最小风险Bayes分类器的设计方案。在参考例程的情况下，自行完善了在一定先验概率的条件下，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。 调用最小风险贝叶斯分类器决策子函数时，根据先验概率，再根据自行给出的5*5的决策表，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，放入决策数组中。 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”，调用最小风险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，就可以得到在一定先验概率条件下，决策表中不同决策的错误率的统计。 (This is a pattern recognition classifier minimum risk Bayes design .In reference to the case of routine , self- improvement in a certain a priori probability 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 The third step is the 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 . Bayesian classifier )