连续相位调制解调

som nemericals eith matlab examples

用于对IRIS数据进行分类的各种分类器，用于对多维采样点进行无监督分类。可根据类别数修改分类器，模式识别作业的部分代码。(IRIS data for the various classification categories, for sampling points on the multi-dimensional non-supervised classification. Can be modified in accordance with several types of classifiers, pattern recognition part of the operating code.)

该代码用于在视觉智能监控/视频监控/视觉监控/视频智能监控中，消除阴影。阴影消除对视频监控具有重要的意义，能够有效地降低阴影对目标检测的影响，减小误检率和漏检率，提高对目标的识别效率以及监控系统的稳定性和可靠性。(This code is used to remove shadow in visual intelligent surveillance/video surveillance/visual surveillance/video intelligent surveillance. Shadow removal plays a important role in video surveillance. It reduce the affection of shadow in object detection, decrease false detection rate and missing rate, and improve the stability and reliability of surveillance system.)

this code implementation chirpaftermatchedfilter

用MATLAB实现灰色预测GM(1，1)模型(Gray prediction GM (1,1) model in MATLAB)

Matlab Program includes Wiener filter iMPLEMENTATION vERY USEFUL

这是模式识别中最小错误率Bayes分类器设计方案。 自行完善了在不同先验概率的条件下，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。 调用最小错误率贝叶斯分类器决策子函数时，根据先验概率数组，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。 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”，调用最小错误率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，就可以得到不同先验概率条件下错误率的统计。 (This is the minimum error rate pattern recognition Bayes classifier design. Self- improvement prior probability in different 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 third step is to 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 . Call the minimum error rate decision Functions Bayesian)

运用matlab的攻击水平机动目标比例导引三维弹道仿真(Attacks using matlab proportional navigation for maneuvering target level of three-dimensional trajectory simulation)

Graphics of PID Control of temperature

微电网遗传算法优化，33节点的配电网网络重构，适合初学者学习(Microgrid genetic algorithm optimization, 33 node distribution network reconfiguration, suitable for beginners to learn.)