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26-69
《MATLAB时尚百例实例》实例26-69()
- 2007-07-11 15:00:20下载
- 积分：1
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041751201034552
说明： 包含蓄电池的微电网的并网模型下的经济调度优化(Optimal Dispatching of Microgrid)
- 2019-01-06 14:33:00下载
- 积分：1
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B_curve
说明： matlab 闭曲线B样条的拟合，算例可直接使用，使用时只需要修改nq文件或者aline文件中的u1即可，也可定义u1后直接run aline(matlab closed B-spline curve fitting, numerical example can be used directly, using only a need to amend the nq documents or document aline can u1, u1 can be defined also directly run aline)
- 2008-10-27 10:41:26下载
- 积分：1
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ANFIS_BP-master
说明： 自适应模糊神经网络的一个模型matlab语言实现(ANFIS_BP-master Substractive clustering algorithm)
- 2019-04-02 15:54:52下载
- 积分：1
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LMS和RLS自适应算法性能比较-comp_lr
LMS和RLS自适应算法性能比较(The comparation of LMS and RLS in adapinve signal processing)
- 2005-02-17 15:57:07下载
- 积分：1
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matrik_class
simple c++ matrik operation class use dynamic allocation memory
- 2009-11-09 01:26:18下载
- 积分：1
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ex3_1
Particle swarm optimization has been used to solve many optimization problems since it was
proposed by Kennedy and Eberhart in 1995 [4]. After that, they published one book [9] and
several papers on this topic [5][7][13][15], one of which did a study on its performance using
four nonlinear functions adopted as a benchmark by many researchers in this area [14]. In PSO,
each particle moves in the search space with a velocity according to its own previous best
solution and its group’s previous best solution. The dimension of the search space can be any
positive integer.
- 2011-02-14 18:37:22下载
- 积分：1
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FFT_wavelet
说明： 该课件讲解了小波与fft发展历程以及应用，对于图像处理有一定的帮助！(wavelet，fft)
- 2010-04-21 21:48:17下载
- 积分：1
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GSO
glowworm swarm optimization algorithm matlab(glowworm swarm optimization algorithm)
- 2015-03-15 21:02:02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
