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a1128_2_No
用matlab读入各个通道载荷数据，计算最大幅值应力，并保存。没有次数。(max load )
- 2013-11-30 22:11:24下载
- 积分：1
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MATLAB-CALCULATOR
SOURCE CODE OF CALCULATOR WITH MATLAB
- 2013-05-15 04:22:56下载
- 积分：1
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GMMspeakers
用高斯混合模型来实现说话人识别的源代码，识别效果挺好的(Using Gaussian mixture model to achieve the source code for speaker recognition, recognition results in very good shape)
- 2010-06-12 13:26:14下载
- 积分：1
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testdata
LPCC FUNCTION matlab and signal processing toolbox for the lpc function
- 2009-04-02 07:20:32下载
- 积分：1
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rn_3
OPTIMAZATION TOOLBOOKS
- 2010-11-28 22:55:22下载
- 积分：1
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mokz
本实验要求在学生掌握模糊控制器基本工作原理和设计方法基础上，熟悉MALAB中的模糊控制工具箱，能针对实际问题设计模糊控制器，建立模糊控制系统，训练学生综合运用计算机来解决一些实际问题的能力。(This experiment requires students to master the basic working principle of the fuzzy controller design method based on MALAB familiar toolbox of fuzzy control to practical problems for the design of fuzzy controllers, fuzzy control systems, integrated use of computers to train students to solve some practical problems.)
- 2009-05-22 11:46:43下载
- 积分：1
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Matlab01
MATLAB GUIDE NO.1 KOR VER.
- 2011-01-06 10:50:16下载
- 积分：1
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haar_face-detection
this is a face detector using haar like features
this is an extension of viola jones model with lesser number of features or subset of features
- 2011-06-09 23:38:00下载
- 积分：1
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kalman
opencv3.0实现的卡尔曼滤波鼠标移动预测(the sourse of kalman)
- 2015-01-31 16:08:08下载
- 积分：1
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cluster VMDaFCM casedat
为了精准、稳定地提取滚动轴承故障特征，提出了基于变分模态分解和奇异值分解的特征提取方法，采用标准模糊C均值聚类(fuzzy C means clustering, FCM)进行故障识
别。对同一负荷下的已知故障信号进行变分模态分解，利用
奇异值分解技术进一步提取各模态特征，通过FCM形成标准聚类中心，采用海明贴近度对测试样本进行分类，并通过计算分类系数和“卜均模糊嫡对分类性能进行评价，将该方法
应用于滚动轴承变负荷故障诊断。通过与基于经验模态分解的特征提取方法对比，该方法对标准FCM初始化条件小敏感，在同负荷故障诊断中表现出更好的分类性能 变负荷故障诊断时，除外圈故障特征线发生明显迁移，其他测试样本故障特征线仍在原聚类中心附近，整体故障识别率保持在100 ，因此，该方法能精确、稳定提取故障特征，为实际滚动轴承智能故障诊断提供参考。( In order to extract fault features of rolling bearing precisely and steadily, a method which is based on variational mode decomposition(VMD) and singular value
decomposition was proposed for fault diagnosis using standard
fuzzy C means clustering(FCM). First of all, the known fault signals measured in the same load but with different faults were
decomposed by VMD, and the modes characteristics were
further extracted using singular value decomposition technique,forming the standard clustering centers by FCM, and then the
test samples were clustered by a Hamming nearness approach,
and the classification performance was uated by calculating classification coefficient and average fuzzy entropy. At last, the method was applied in rolling bearing fault diagnosis under
variable loads. By comparing with a method based on EMD,
this approach is not sensitive to the initialization of standard FCM, and exhibits better classification performance in the same load fault diagnosis Fo)
- 2020-11-11 20:59:44下载
- 积分：1
