matlab

通信里的2ASK信号的产生及分析，很不错，经典实用(Communication in the 2ASK signal generation and analysis, very good, classic and practical)

说明：  利用MATLAB实现模拟退火算法计算商旅问题(Use MATLAB to achieve simulated annealing algorithm Business Problems)

SAR成像经典MATLAB仿真程序，三种不同的算法，学习雷达成像的学生可以考虑下载(sar imaging by cs)

纹理特征提取的matlab源代码，用灰度共生矩阵做的，最终得到一个8维的纹理特征向量。(Texture feature extraction matlab source code, the GLCM do eventually get an 8-dimensional texture feature vector.)

迭代小程序，可以根据参数变化导出倍周期分叉图，从而研究有序与混沌的关系。(Iterative small program that can export period doubling bifurcation diagram according to the parameter changes, which studies the relationship between order and chaos.)

离散对称信道的信道容量的计算离散对称信道的信道容量的计算(Discrete symmetric channel is the channel capacity of computing discrete symmetric channel calculation of channel capacity)

Matlab Image toolbox example

三维各向异性有限差分地震波模拟程序，用matlab编写(The three-dimensional anisotropic finite-difference seismic wave simulation program using matlab)

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 )

说明：  apso求解电力系统经济调度问题，可以得到每个机组的出力，并且含有多个约束条件(APSO can solve the economic dispatch problem of power system and get the output of each unit, which contains many constraints)