-
get_sofea
基於MATLAB的有限元GUI介面，可分析應力與熱傳(MATLAB-based GUI interface for finite element analysis, stress and heat transfer can be analyzed)
- 2010-05-24 12:02:18下载
- 积分：1
-
gaborfilter
Gabor filter algorithms in matlab
- 2011-05-24 19:50:51下载
- 积分：1
-
window_fir
说明： 本压缩文件包含了用MATLAB做的几种fir滤波器，功能强大，可用性强！(This compressed file contains the use of MATLAB to do some fir filter, powerful, and the availability of strong!)
- 2010-05-01 22:33:53下载
- 积分：1
-
huise
灰色模型 可以用来预测一段时间变量的变化量(The Gray model can be used to predict the amount of change of the period of time variable)
- 2013-02-02 15:01:14下载
- 积分：1
-
CA-CFAR
CA-CFAR单元平均恒虚警算法在杂波边缘环境下的性能(The performance of CA-CFAR under clutter edge environment)
- 2012-12-16 10:25:04下载
- 积分：1
-
noise-signal
求任意带宽的低通、高通、带通高斯白噪声的自相关函数，自协方差函数和功率谱密度函数。(Seeking any bandwidth low pass, high pass, band pass Gaussian white noise autocorrelation function, autocovariance function and power spectral density function.)
- 2021-03-22 20:29:16下载
- 积分：1
-
BlockedBaseDCTDataHiding
Block base DCT data hiding and recovery of secrete image.
- 2013-10-29 18:18:54下载
- 积分：1
-
Road_imput_for_StateSpace_V2
4轮随机路面时域输入,根据路面功率谱，随机生成时域路面文件(4 wheel randm road input)
- 2020-12-15 15:39:14下载
- 积分：1
-
quad
说明： 一个导弹拦截simulink仿真程序，内含目标运动函数、导弹运动模型、导弹制导与控制算法(A Simlink Simulation Program for Missile Interception)
- 2021-04-06 22:39:02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
