matlab_moshishibie

盲信号分离算法的实现和应用 大家来看看，应该对大家有点用，不是很长，如果有需要还有别的来发(Blind Signal Separation Algorithm Implementation and Application of everyone to see, we should use a little, not very long. If there is a need to have something else to fat)

对于实际数据进行SAR成像CS算法的代码(Code for SAR imaging CS algorithm of actual data)

双目标 NSGAII 算法案例(A case of double target NSGAII algorithm)

用于控制系统的优化，自动对输入系统进行超前滞后调节，并会自动作出bode图。(Modeling assitance program, using lead and lag to modefy the system)

采用matlab编程语语言，可在二维图片（对称）移动鼠标，自动检测出边缘轮廓，将二维图片转化为3维立体图片。(Using MATLAB programming language, the mouse can be moved in two dimensional picture (symmetrical), the edge contour is detected automatically, and the two-dimensional picture is transformed into 3 dimensional stereoscopic picture.)

optimation method DFP

matlab实现产生二进制随机序列函数，包括概率分布(matlab generate binary random sequence to achieve the function, including the probability distribution)

哈夫曼压缩算法，开发环境Matlab，利用此例程序可以仿真哈夫曼压缩比(Huffman compression algorithm development environment of Matlab, using this example program can simulate the Huffman compression ratio)

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：  neural network falut finding