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CCDF curve in MATLAB

自己弄的多端直流配电网的仿真模型，是支撑自己小论文的东东。仿真环境是用的matlab。(Get yourself a simulation model with a multi-terminal DC power grid, is supporting their own small paper stuff. Simulation environment is used matlab.)

matlab 光谱处理方法多元散射校正！(matlab spectrum treatment of multiplicative scatter correction!)

说明：  《载波相位相对定位双差方程原理》验证 《载波相位相对定位单差方程原理》验证 《载波相位相对定位双差方程原理》验证 《载波相位相对定位双差方程原理》验证当基站离流动站很近，并假设流动站概略坐标等于基站坐标，那么双差方程中距离向量就为0(&quot Double difference carrier phase relative positioning equation theory&quot test &quot single-difference carrier phase relative positioning equation theory&quot test &quot double difference carrier phase relative positioning equation theory&quot test &quot double difference carrier phase relative positioning equation principle&quot is verified when the base station from the mobile station near, and assuming that mobile station is equal to the base station coordinates approximate coordinates, so double-differential equation from the vector is 0)

逐行读取GPS观测文件中的信息，实现提取站坐标，L,P观测值等各项观测值，生成一个观测值矩阵。(GPS observation file line by line to read the information, to achieve extraction station coordinate, L, P observations and other observations, to generate a matrix of observations.)

多组分shan-Chen伪势模型，可用于求解气液分离，是LBM的基础。(Multi component shan-Chen pseudo potential model)

产生随机导频信号的程序 能利用此基本的来帮助进行复杂的编程(Generate random pilot signal)

说明：  matlab编写的训练som神经网络源程序 (Matlab prepared by the neural network training som source)

此为SVM支持向量机，下载后放入matlab工具箱里，即可使用！(This is the SVM support vector machine, after download in matlab toolbox, can use!)

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)