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matlab
本章将涉及比较深层的 MATLAB 内容:脚本;函数(一般函数、内联函数、子函数、
私用函数、方法函数);程序调试和剖析;数据结构(类、对象);重载和继承;面向对象
编程。本章配备了许多精心设计的算例。这些算例是完整的,可直接演练的。读者通过这些
算例,将真切感受到抽象概念的内涵、各指令间的协调,将从感知上领悟到面向对象编程的
优越和至关要领。(MATLAB )
- 2010-03-09 22:30:34下载
- 积分:1
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caiji
说明: VC_中利用AVICAP_DLL实现图像采集(VC_ for image acquisition using AVICAP_DLL)
- 2010-04-17 17:57:26下载
- 积分:1
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EdgeDetection
基于matlab的边缘检测,方法种类很多,如,sobel,canny,prewitt等等(Edge detection methods,such as,soble,canny,prewitt,and so on.)
- 2010-05-20 19:40:36下载
- 积分:1
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OpAmp
opamp simulink in matlab
- 2013-04-09 18:37:34下载
- 积分:1
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BVAR
var模型的实现。针对var模型的算法,估计模型的参数。并对其进行检验(achieve var model. Var model for the algorithm, the estimated parameters of the model. And its test)
- 2013-12-03 16:45:28下载
- 积分:1
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officeassign
用最优化方法求解优化问题的极大值或极小值问题的方法(Maximum or minimum value of the issue of method optimization method for solving optimization problems)
- 2015-01-10 22:35:23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
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zuixiaoercheng
实现基于加权最小二乘法的电力系统状态估计算法,以于老师写的书里面四节点算例仿真(Implementation based on weighted least-square method of power system state estimation algorithm, with the teacher wrote the book four node example simulation)
- 2020-08-26 10:18:13下载
- 积分:1
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rfft
fft calculation of any input signal
- 2010-11-17 15:11:11下载
- 积分:1
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MatlabSkyJournal4
说明: MATLAB技术论坛电子期刊第四期,matlabksy专业MATLAB技术交流平台!(MATLAB Technical Forum Electronic Journal IV, matlabksy professional technical exchange platform for MATLAB!)
- 2011-03-07 20:24:03下载
- 积分:1