Sum_rate-maximization-

数值分析最常用的四十种算法，使用c++/matlab实现(numerical analysis of the 40 most commonly used algorithms, the use of c/Matlab achieve)

计算两个离散变量x和y的条件熵z = H（x | y）。 with Input：两个相同长度的整数向量 有益于学习(Compute conditional entropy z=H(x|y) of two discrete variables x and y. with Input: of two integer vector of the same length good for learning)

产生模拟信号的程序，是我自己写的，毕业设计的时候用到的(Procedures generate analog signals that I wrote it myself, when used in graduate design)

辐射边界和定温边界条件下，二维矩形平板的温度场数值模拟。(Radiation boundary and constant temperature boundary conditions, two-dimensional rectangular plate temperature field numerical simulation.)

Function to simulate a crane and its load and strain

UTV分解工具包,常用的相关matlab程序(tools of UTV)

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 )

ga优化神经网权值&阈值程序。为了方便，我把几个matlab的程序都放在txt文件里，使用时，对照把txt内容复制进matlab即可(ga optimization neural network weights & threshold procedures. For convenience, I have several matlab programs on the txt file, use, control the content of the txt you copied into matlab)

it deals with test attractor, which is related to fractal.

自己编写的一维电磁波由自由空间进入损耗介质，全匹配环境下的传输.(I have written a one-dimensional electromagnetic waves from free space into a lossy medium, the whole match the transmission environment.)