-
FDTD
一种实现电磁波的FDTD仿真模拟的源程序代码(An FDTD simulation of electromagnetic waves of the source code)
- 2007-08-31 20:12:08下载
- 积分：1
-
designing_of_iir_lowpass_filter
Designing a low pass IIR filter
- 2010-09-25 01:05:45下载
- 积分：1
-
DrivingFunctionswfs
Computes driving functions..hope you like it
- 2010-12-16 19:30:10下载
- 积分：1
-
MFC_matlab
程序实现了C++和MATLAB的混合编程，对接的方法是使用Matcom组件。程序实现的功能是，绘制一个sin函数。(Program implements C++ and MATLAB mixed programming, docking method is to use Matcom components. Program' s function is to draw a sin function.)
- 2013-08-13 16:56:57下载
- 积分：1
-
KS-RS
主要用于样本的划分，有K-S和R-S两种方法(Mainly used for sample classification, there are two ways to KS and RS)
- 2020-11-19 21:59:38下载
- 积分：1
-
Chapter2ReadMe
simplified code for
- 2010-10-17 05:50:50下载
- 积分：1
-
MATLABprogram
基于Matlab环境编写的一些神经网络PID控制和模糊PID控制源代码，其中包含BP pid，CMAC PID,RBF PID，BP数值逼近算法，BP预测控制以及模糊PID。(Matlab-based environment for the preparation of a number of neural network PID control and fuzzy PID control of the source code, which includes BP pid, CMAC PID, RBF PID, BP Numerical approximation algorithm, BP predictive control and fuzzy PID.)
- 2009-05-11 20:25:12下载
- 积分：1
-
modulate_demodulate
实现信号的DSB,FM，PM,FSK的调制与解调，同时包含加噪声处理。(Implement signal DSB, FM, PM, FSK modulation and demodulation, while including additional noise processing.)
- 2020-07-03 09:20:01下载
- 积分：1
-
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
-
fgf
学习的一个产物，供大家作为实验的参考，不好的地方见谅(A product of learning for us as a reference experiment, where poor excuse me)
- 2010-05-12 10:43:30下载
- 积分：1
