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详细介绍MATLAB中s函数的用法，帮助初学者掌握s函数(S introduction of a detailed MATLAB function usage to help beginners master s function)

说明：  用MATLAB读取、写入和更改SQL数据库中数据(Use MATLAB to read, write and change data in SQL database )

关于matlab基础操作、矩阵运算、程序设计、绘图等的学习资料(Learning materials of the underlying operating on matlab, matrix operations, program design, graphics, etc.)

Matlab Implementation of Standard PageRank method on sampled SNAP Google dataset.

用四元十字阵做被动声定位算法设计，现在是用matlab神经网络工具箱构建RBF神经网络然后仿真显示图形(With a four-element Array do passive acoustic localization algorithm design, now using matlab neural network toolbox and then build on RBF neural network simulation display graphics)

基于matlab的有限元程序源代码，希望大家喜欢(Based on the finite element program matlab source code, I hope everyone likes)

Finite Volume Method 流体仿真代码 利用TDMA算法计算二维矩阵 从而计算出二位场内各点的性质 可以通过修改步长和网格大小实现算法研究(A computational fluid dynamics code for determine the property of a two-dimensional plate by using Finite Volume Method. Grid size and time step are free to change in this code.)

其中包括3个c++搜索源程序和2个matlab编写的fdtd程序(including three c search source and two Matlab procedures prepared by the fdtd)

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)

合成孔径雷达成像 算法与实现光盘数据及处理步骤(digital processing of SAR)