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voice
语音识别中几个常用例程：
语音信号高频提升、短时能量检测、过零率计算、LPCC系数计算、基于短时能量和过零率的语音信号端点检测。(voice recogniton)
- 2011-05-25 16:41:07下载
- 积分：1
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CH4programme
说明： 神经网络 侯媛彬 西安电子科技大学出版社 全书程序夹CH4(Neural network)
- 2010-04-30 10:35:53下载
- 积分：1
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sanxiangquankongzhengliudianlu
三相可控硅全控整流 功率因数 matlab仿真 工业控制 (
Three-phase silicon controlled rectifier all control the power factor matlab simulation industrial control)
- 2011-10-07 00:41:53下载
- 积分：1
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输出AMI码和HDB3码TONGXIN
通信原理实验，输入任意0，1码，输出AMI码和HDB3码(Communication principle experiment, AMIandHDB3 code)
- 2020-07-05 13:28:59下载
- 积分：1
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mpc_4
this is matlab code
- 2014-01-30 03:36:43下载
- 积分：1
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tracking
这是用C语言编写的机动目标跟踪程序，用matlab仿真实现的。直接用于机动目标跟踪仿真。(This is written with C language target tracking procedures implemented with the matlab simulation. Directly used for maneuvering target tracking simulation.)
- 2021-01-11 14:28:49下载
- 积分：1
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Digital-Smith-pre-estimate-control
根据smith预估计控制算法，使用MATLAB编写程序实现控制(According to pre-estimate smith control algorithms using MATLAB programming for control)
- 2014-08-11 22:39:51下载
- 积分：1
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ode4
经常看到很多朋友问定步长的龙格库塔法设置问题，下面吧定步长四阶龙格库塔程序贴出来，有需要的可以看看
(Often see many of my friends ask determining step Runge-Kutta method set problem, the following step you set the fourth-order Runge-Kutta procedures paste out, there is a need to look at the)
- 2008-06-10 17:54:10下载
- 积分：1
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emd_huang
说明： hht变换中的关键部分emd分解，集中了hht变换中的最精华的部分。(hht transform a key part of emd decomposition, concentrated hht transform the most essential part.)
- 2010-04-04 21:09:54下载
- 积分：1
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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
