shengwenshibiequandaima

关于matlab的gui设计,介绍了关于matlab gui设计的基本知识，对于初学者非常有用(matlab gui design. It introduces the basic knowledges of gui design. It is very useful for matlab beginners.)

把遗传算法和退火算法相结合，寻找图像分析所需的阈值。(genetic algorithms and annealing algorithm combining image analysis to find the required threshold.)

ＲＬＳ算法用ＭＡＴＬＡＢ实现语音去噪，包含源程序注释，方便易懂(RLS algorithm with MATLAB voice denoising)

in this package are the programs needed to solve any problems differential linear boundary value.

matlab, bahasa pemrograman

电脑上保存的一些vc和MATLAB混合编程的资料，其中还有vc和matcom接口的视频演示。(Computer, save some of the vc and MATLAB mixed programming information, which are vc and matcom interface video presentation.)

OFDM的调制代码，对于OFDM的调制可以加深理解(OFDM modulation code, for better understanding of OFDM modulation can be)

Differential of Functions like Sin , Cos , Tan , Cot and other Functions with Central Oh4 method and calculation of errors Differential with matlab program. Saman Darvish

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：  雷达信号处理 雷达作用距离 功率，距离，RCS(Pulse compression simulation MATLAB version, GUI procedures)