lvboqi

给出了用中值滤波器去噪的matlab程序(give a denoise program on matlab by median filter )

说明：  128QAM调制仿真的星座图。matlab源文件。(Simulation 128QAM modulation constellation diagram. matlab source file.)

MATLAB工具箱的神经网络理论与应用程序源代码 丛爽编，国科学技术大学出版社(Toolbox for MATLAB neural network theory and application source code is compiled Cong Shuang, State University of Science and Technology Press)

branch-and-bound-tree algotihm for optimization

fisher 图片处理报告，程序搜索Fisher program(fisher image processing reports, program searches Fisher program)

电力系统无功对网损的灵敏度，包含雅可比矩阵，各种函数公式(the power system)

成功实现计算OVYnqF算法的程序，本人测试已经通过，性能已基本达到OVYnqF算法的性能，需要MATLAB 2009b环境运行，如有问题，欢迎讨论 (The successful implementation of the program to calculate OVYnqF algorithm, I have passed the test, need MATLAB2009b operating environment, any questions, please contact me )

ICA算法评价指标的计算程序SAR SIR SNR(ICA SAR SIR SNR)

势阱中的波函数,matlab程序,彭芳鳞教材(The wave function in the potential well)

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 )