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利用matlab编程实现解线性方程组的迭代法。(using Matlab programming for solving linear equations of iterative method.)

matlab 离散序列FFT求平均周期程序(matlab discrete FFT sequence of procedures for the average cycle)

portfolio optimization

一种新的函数拟合方法，在某些问题的近似程度上甚至优于径向基插值和Kriging插值方法(A new function fitting method, even better than the approximate extent of some of the issues radial basis interpolation and Kriging interpolation method)

说明：  MATLAB短时傅里叶变换和morlet小波变换(short-time Fourier transform and morlet wavelet transform based on matlab)

说明：  为了了解地震波的传播，用有限差分对二维声波传播进行模拟(Solving wave wave equation with finite difference)

说明：  this file is about risk managment in marketing....

多址干扰一直是造成cdma系统容量限制的重要原因，本程序主要模拟了多址干扰对每个用户的影响以及把多址干扰作为高斯白噪声看待时对用户的影响(MAI has been caused CDMA system capacity limit of the important reasons the procedures simulated the MAI for each user to the effects of MAI as a Gaussian white noise when users view the impact)

EMTP: Expectation conditinal Maximazation either Thresholding Pursuit Coded by Heping Song

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 )