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Program-for-solving-laplace-equation-using-FDM
Program for solving Laplace Equation
- 2011-05-09 22:22:57下载
- 积分：1
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Diophantine_1_Thrm
2 m-files in matlab to help identify the if the function is a Diophantine equation(Two useful m-files for the Diophantine equation identification. )
- 2012-07-20 01:24:48下载
- 积分：1
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Matlab_MSCT_version1
多尺度的压缩感知跟踪matlab源码，效果还不错，亮点在于解决多尺度变化的问题(Multi-scale compressed sensing tracking matlab source, the results were good, highlights that solve the problem of multi-scale changes)
- 2020-12-05 01:09:24下载
- 积分：1
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calib212
digital modulation techniques
- 2009-05-04 08:55:46下载
- 积分：1
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ModelIdentification
关于模型辨识的MATLAB仿真源码。有使用最小二乘的建模，有极大似然估计建模的方法。每个重点例句都有详细的解释。(On the MATLAB simulation model of source identification. Modeling the use of least squares, and maximum likelihood estimation method of modeling. Each key has a detailed explanation of examples.)
- 2009-06-10 07:22:21下载
- 积分：1
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masy2
this file about induction moteur part 2
- 2012-05-07 17:16:14下载
- 积分：1
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AODV_2
基于地理位置的距离贪心路由协议GPSR的matlab仿真代码(Matlab simulation code based on geographical distance greedy routing protocol GPSR)
- 2012-09-04 10:49:11下载
- 积分：1
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112358lyapunov_wolf
wolf求最大Lyapunov指数，方法简单，速度快，便于理解。(wolf biggest Lyapunov exponent, the method is simple, fast, and easy to understand.)
- 2013-04-14 23:25:23下载
- 积分：1
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NewK-means-clustering-algorithm
说明： 珍藏版，可实现，新K均值聚类算法，分为如下几个步骤：
一、初始化聚类中心
1、根据具体问题，凭经验从样本集中选出C个比较合适的样本作为初始聚类中心。
2、用前C个样本作为初始聚类中心。
3、将全部样本随机地分成C类，计算每类的样本均值，将样本均值作为初始聚类中心。
二、初始聚类
1、按就近原则将样本归入各聚类中心所代表的类中。
2、取一样本，将其归入与其最近的聚类中心的那一类中，重新计算样本均值，更新聚类中心。然后取下一样本，重复操作，直至所有样本归入相应类中。
三、判断聚类是否合理
采用误差平方和准则函数判断聚类是否合理，不合理则修改分类。循环进行判断、修改直至达到算法终止条件。(NewK-means clustering algorithm ，Divided into the following several steps:
A, initialize clustering center
1, according to the specific problems, from samples with experience selected C a more appropriate focus the sample as the initial clustering center.
2, with former C a sample as the initial clustering center.
3, will all samples randomly divided into C, calculate the sample mean, each the sample mean as the initial clustering center.
Second, initial clustering
1, according to the sample into the nearest principle clustering center represents the class.
2, as this, take the its recent as clustering center of that category, recount the sample mean, update clustering center. And then taking off, as this, repeated operation until all samples into the corresponding class.
Three, judge clustering is reasonable
Adopt error squares principles function cluster analysis.after clustering whether reasonable, no reasonable criterion revisio)
- 2011-04-06 20:45:56下载
- 积分：1
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OMP
二维omp算法，初学者建议看看，对学习很有帮助(Two dimensional OMP algorithm, a simple application)
- 2015-03-17 16:13:05下载
- 积分：1
