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pvmpptVhill
mppt hill climbing with solar cill model
- 2010-10-27 01:36:54下载
- 积分:1
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Ok
说明: OK算法的Matlab计算包,包含了验证和拟合的过程(OK computing package Matlab algorithms, including the process of validation and fitting)
- 2011-01-11 20:03:08下载
- 积分:1
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PSO
说明: 用粒子群算法计算最短路径,一般用于车辆路径问题(PSO)
- 2010-04-28 16:30:26下载
- 积分:1
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12jj
说明: 最小二乘法直线拟合在matlab的应用 (Least Squares Linear Fitting)
- 2010-04-29 15:13:15下载
- 积分:1
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shannon
信息论中变长编码的香农编码程序,大家可以互相学习(In the information theory of shannon length coding encoding process, we can learn from each other)
- 2011-12-28 19:16:45下载
- 积分:1
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OSTU
是自适应阈值的分割,主要对图像用matlab进行分割图像,确定图像想要的东西(Adaptive segmentation threshold, the main image to image segmentation using matlab, determine what image you want)
- 2014-01-05 18:13:18下载
- 积分:1
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kftool_matlab
kalman filter toolbox for matlab...
- 2010-12-06 00:58:00下载
- 积分:1
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QPSK123
qpsk信号瑞利信道的仿真程序 有ber仿真效果图 推荐(Rayleigh channel simulation program )
- 2013-11-14 12:56:32下载
- 积分:1
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Complete
a doctorate thesis for dynamic multi-objective optimization
- 2015-01-18 16:44:08下载
- 积分:1
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Minimum-Risk-Bayes-classifier
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 )
- 2012-02-02 20:37:04下载
- 积分:1