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gaussian
Gaussian Distribution.
- 2009-12-22 23:46:24下载
- 积分:1
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dip_final
this is the matlab code for dip lab programs
- 2011-04-26 13:05:45下载
- 积分:1
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Solving-DDEs-in-matlab
matlab求解时滞微分方程教程,很详细,有例子。(Solving delay differential equations matlab tutorial)
- 2013-07-18 22:14:08下载
- 积分:1
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bingwang
下垂控制,两个微电源,孤岛运行和并网运行(droop control,two DG )
- 2021-03-02 20:49:34下载
- 积分:1
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assignment1
通过对输入ISBN进行演算.从而得出其真伪信息(Input to calculations by ISBN. To arrive at the authenticity of the information)
- 2010-03-01 01:07:39下载
- 积分:1
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DTC
异步电动机数量控制完整仿真模型,可以根据实际情况写该参数,达到研究者所需要的情形(The number of complete induction motor control simulation model, the parameter can be written according to the actual situation, the researchers needed to achieve the situation)
- 2013-12-17 13:00:49下载
- 积分:1
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matlab
matlab讲义!各种基本知识点和使用方法,源程序用法!(matlab handouts! Basic knowledge and use, source usage!)
- 2012-11-30 09:12:14下载
- 积分:1
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Corner_Matching_by_Invariant_Moments
Corner_Matching_by_Invariant_Moments.rar
INDEX:
1- InvariantMomentsofCorners.m
Calculate moment invariants on the selected window of the corner.
2- invariantmoments.m
Calculates Moment invariants
- 2009-09-25 22:07:55下载
- 积分:1
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dftMATLAB
DFT:离散傅立叶变换
符运行后得频谱图(DFT: Discrete Fourier Transform running at a frequency spectrum)
- 2008-04-23 01:42:35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