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Cbeam
悬臂梁的振动特性问题,解决了悬臂梁的固有频率以及模态振型(C-F beam,freq,mode)
- 2020-11-23 21:09:33下载
- 积分:1
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file
armold 变换是数字图像置乱中常用的一种方法。本文给出了armold 变换的matlab源代码,以方便大家的使用。(armold digital image scrambling transformation is commonly used methods. In this paper, armold transform matlab source code to facilitate everyone' s use.)
- 2010-05-05 15:53:30下载
- 积分:1
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optical-prop.-of-ellipsoid
optical properties of nano metallic ellipsoid, with different incident angels
- 2011-10-01 17:51:30下载
- 积分:1
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mohuzuoyezhu
模糊控制作业,采用模糊控制方法实现,闭环控制,并与传统PID控制比较说明模糊控制的优越性(Operations of fuzzy control, fuzzy control method to achieve closed-loop control, and comparison shows the superiority of fuzzy control and conventional PID control)
- 2020-12-18 11:09:11下载
- 积分:1
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matlab55
学会不同搜索法(最速下降法,共轭梯度法,牛顿法,拟牛顿法)的计算步骤;
比较不同搜索法(最速下降法,共轭梯度法,牛顿法,拟牛顿法)的优缺点。
(Institute of the different search method (steepest descent method, conjugate gradient method, Newton method, quasi-Newton method) for calculating steps compare different search method (steepest descent method, conjugate gradient method, Newton method, quasi-Newton method) the advantages and disadvantages.)
- 2008-04-13 19:40:44下载
- 积分:1
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fnn
false nearest neighbor calculation on a signal as a chaotic indicator
- 2013-07-18 01:06:03下载
- 积分:1
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maslov-rewiring
maslov的保持度以及度分布不变,随机网络边的算法。(retention maslov and distribution constant, a section of a random network algorithm.)
- 2013-04-22 14:13:44下载
- 积分:1
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TSP
免疫克隆选择算法打求解ts问题 可直接运行 功能强大(Immune clonal selection algorithm fight solving ts problems can be directly run powerful)
- 2013-04-23 19:30:13下载
- 积分:1
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quantumpso-(2)
this is a matlab code for quantom pso algorithm coding.
- 2013-12-14 05:27:34下载
- 积分:1
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knn1
K最邻近密度估计技术是一种分类方法,不是聚类方法。
不是最优方法,实践中比较流行。
通俗但不一定易懂的规则是:
1.计算待分类数据和不同类中每一个数据的距离(欧氏或马氏)。
2.选出最小的前K数据个距离,这里用到选择排序法。
3.对比这前K个距离,找出K个数据中包含最多的是那个类的数据,即为待分类数据所在的类。(K nearest neighbor density estimation is a classification method, not a clustering method.
It is not the best method, but it is popular in practice.
Popular but not necessarily understandable rule is:
1. calculate the distance between the data to be classified and the data in each other (Euclidean or Markov).
2. select the minimum distance from the previous K data, where the choice sorting method is used.
3. compare the previous K distances to find out which K data contains the most data of that class, that is, the class to which the data to be classified is located.)
- 2017-08-09 21:06:38下载
- 积分:1