wavelettransform

高斯混合模型用于混合像元分解全过程，包括训练样本选取、筛选、分解及精度评定。matlab实现(the matlab code of GMM for mixed pixels unmixing)

说明：  用MATLAB编写的粒子群基本算法－PSO算法来求无约束函数的极值问题，得出比较好的结果(MATLAB prepared using the basic algorithm PSO-PSO algorithm to seek non-binding function of the extremal problem, arrive at some more good results)

通过MATLAB软件所建立的CRH2动车的模型。(Established through MATLAB software model CRH2 EMU.)

带通滤波函数，故障诊断时采集振动信号后对信号进行滤波后处理。(The signal is filtered after the signal is collected in the fault diagnosis)

LTE上行接入技术SC-FDMA的papr，其中包含采用不同调制方式和集中式、分布式接入技术的papr(LTE SC-FDMA uplink access technology of papr, which contains a different modulation mode and centralized, distributed access technology papr)

simulation of 3_phase rectifier in matlab simulink

灰色系统相关程序，可用于对事物规律的预测或进行灰色评价。(grey sysdem)

太阳能电池板VI曲线建模 用matlab的simulink工具 可视曲线(Simulink tool solar panels VI curve modeling using matlab visual curve)

子空间算法MUSIC算法的matlab实现程序(MUSIC Algorithm)

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)