lorenzeq

这是计算方法基本迭代法的一个matlab编程实例。(This is the calculation of a basic iterative method matlab programming examples)

这是一个基于自适应的滑模控制的仿真程序，对于研究滑模控制会有帮助(adaptive slinding mode control)

说明：  RANSAC的MATLAB工具包，MATLAB下实现了多种SAC算法(GML RANSAC Matlab Toolbox v 0.1)

数据优化DPCM方法，对一个余弦信号数据进行标量量化(Data Optimized DPCM method, a cosine signal data scalar quantization)

安捷仑的数字调制信号发生器E4438用于很多通信系统的开发过程中。本代码可以用于通过网络接口或GPIB接口向E4438下载被调制数据(Ansett in 2000 digital modulation signal generator E4438 used in many communications systems development process. The code can be used by the network interface or E4438 GPIB interface to download data to be modulated)

部分全国大学生数学建模竞赛题，及报告，算法很好(The title and the report of part of the National Undergraduate Mathematical Contest in Modeling)

Preconditioned Conjugate Gradient iterative Ax=b solver.Routine to solve a symmetric positive definite linearsystem Ax = b using the Preconditioned Conjugate

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 )

全变差图像重建的交替最小化算法 包含灰度图像，彩色图像去噪去模糊的程序(A new alternating minimization algorithm for total variation image reconstrction)

this program describes the partical filter and extended kalman filter used to reduce the error to get better accuracy of the position