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AHP(RI)
层次分析法(AHP)是进行评估的一种常用方法,其中层次分析法中求随机一致性指标是该方法的关键一步。本程序即实现的求解PI的程序(AHP is a common method of assessment, in which AHP seek random consistency index is a key step in the method. This procedure that is implemented procedures for solving PI)
- 2009-11-07 23:37:38下载
- 积分:1
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Numerical-Analysis-Based-on-MATLAB
科学计算引论-基于MATLAB的数值分析 第二版(本书重点为数值计算方法和计算的可视化)(Introduction to Scientific Computing- MATLAB-based numerical analysis second edition (book focuses on numerical methods and computational visualization))
- 2013-08-26 23:26:35下载
- 积分:1
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LZ78Encode
信息论与编码中LZ系列LZ78编码源文件(LZ series of information theory and coding LZ78 encoding the source file)
- 2012-07-01 17:11:19下载
- 积分:1
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IEEE34-with-wind
介绍关于IEEE34-with-wind及其仿真模型(About IEEE34-with-wind and its simulation model)
- 2013-10-01 19:23:05下载
- 积分:1
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chapterall
现代通信系统使用MATLAB第二版中所有程序m文件(modern communications system MATLAB second edition document all the procedures m)
- 2007-03-14 12:53:24下载
- 积分:1
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GA_TPS
旅行商(TSP)问题的遗传优化算法源代码(Genetic traveling salesman (TSP) problem optimization algorithm source code)
- 2014-11-19 16:17:24下载
- 积分:1
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estimate_Geometric
describe estimate geomatric transfare
- 2014-12-01 04:26:24下载
- 积分:1
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Pysichal-optics
基于高频电磁散射的物理光学法用于计算散射目标的RCS
(Based on high-frequency electromagnetic scattering of the physical optics method used to calculate the scattering target RCS)
- 2020-12-03 11:19:25下载
- 积分:1
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MyKmeans
实现聚类K均值算法: K均值算法:给定类的个数K,将n个对象分到K个类中去,使得类内对象之间的相似性最大,而类之间的相似性最小。 缺点:产生类的大小相差不会很大,对于脏数据很敏感。 改进的算法:k—medoids 方法。这儿选取一个对象叫做mediod来代替上面的中心 的作用,这样的一个medoid就标识了这个类。步骤: 1,任意选取K个对象作为medoids(O1,O2,…Oi…Ok)。 以下是循环的: 2,将余下的对象分到各个类中去(根据与medoid最相近的原则); 3,对于每个类(Oi)中,顺序选取一个Or,计算用Or代替Oi后的消耗—E(Or)。选择E最小的那个Or来代替Oi。这样K个medoids就改变了,下面就再转到2。 4,这样循环直到K个medoids固定下来。 这种算法对于脏数据和异常数据不敏感,但计算量显然要比K均值要大,一般只适合小数据量。(achieving K-mean clustering algorithms : K-means algorithm : given the number of Class K, n will be assigned to target K to 000 category, making target category of the similarity between the largest category of the similarity between the smallest. Disadvantages : class size have no great difference for dirty data is very sensitive. Improved algorithms : k-medoids methods. Here a selection of objects called mediod to replace the center of the above, the logo on a medoid this category. Steps : 1, arbitrary selection of objects as K medoids (O1, O2, Ok ... ... Oi). Following is a cycle : 2, the remaining targets assigned to each category (in accordance with the closest medoid principle); 3, for each category (Oi), the order of selection of a Or, calculated Oi Or replace the consumption-E (Or))
- 2005-07-26 01:32:58下载
- 积分:1
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ExtCertPathBuilderException
public interface CallbackHandler for Andriod.
- 2013-11-26 15:06:55下载
- 积分:1