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DGA-master

于 2021-01-04 发布
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下载积分: 1 下载次数: 4
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代码说明：

说明：  一种用于实现变压器等故障分类的matlab代码(A matlab code for transformer fault classification)

文件列表：

DGA-master, 0 , 2018-04-12
DGA-master\.gitignore, 814 , 2018-04-12
DGA-master\Config.xlsx, 16503 , 2018-04-12
DGA-master\DGA.m, 342 , 2018-04-12
DGA-master\LICENSE.md, 13894 , 2018-04-12
DGA-master\README.md, 519 , 2018-04-12
DGA-master\datasets, 0 , 2018-04-12
DGA-master\datasets\dataset10_(39).xlsx, 11438 , 2018-04-12
DGA-master\datasets\dataset11_(16).xlsx, 10736 , 2018-04-12
DGA-master\datasets\dataset12_(68).xlsx, 12108 , 2018-04-12
DGA-master\datasets\dataset1_(240).xlsx, 17232 , 2018-04-12
DGA-master\datasets\dataset2_(99).xlsx, 12238 , 2018-04-12
DGA-master\datasets\dataset3_(5).xlsx, 9026 , 2018-04-12
DGA-master\datasets\dataset4_(9).xlsx, 9175 , 2018-04-12
DGA-master\datasets\dataset5_(8).xlsx, 9155 , 2018-04-12
DGA-master\datasets\dataset6_(19).xlsx, 9572 , 2018-04-12
DGA-master\datasets\dataset7_(6).xlsx, 9141 , 2018-04-12
DGA-master\datasets\dataset8_(42).xlsx, 25858 , 2018-04-12
DGA-master\datasets\dataset9_(13).xlsx, 10610 , 2018-04-12
DGA-master\datasets\diagnosis_only.xlsx, 25778 , 2018-04-12
DGA-master\datasets\test_clustering.xlsx, 8750 , 2018-04-12
DGA-master\docs, 0 , 2018-04-12
DGA-master\docs\DGA_Document.xlsx, 11178 , 2018-04-12
DGA-master\docs\Tasks.docx, 17380 , 2018-04-12
DGA-master\docs\Tasks.xlsx, 12879 , 2018-04-12
DGA-master\docs\User Manual.docx, 147323 , 2018-04-12
DGA-master\gui, 0 , 2018-04-12
DGA-master\gui\AnalyzeResults.m, 781 , 2018-04-12
DGA-master\gui\Cfg_Datasets.m, 34864 , 2018-04-12
DGA-master\gui\Cfg_Methods.m, 35140 , 2018-04-12
DGA-master\gui\DGALab.m, 60111 , 2018-04-12
DGA-master\gui\DGALab.mat, 569 , 2018-04-12
DGA-master\gui\DGA_Diagnosis.m, 427 , 2018-04-12
DGA-master\gui\DGAs_n.m, 2872 , 2018-04-12
DGA-master\gui\Edit_Datasets.fig, 18698 , 2018-04-12
DGA-master\gui\Edit_Datasets.m, 14532 , 2018-04-12
DGA-master\gui\Edit_Methods.fig, 5476 , 2018-04-12
DGA-master\gui\Edit_Methods.m, 14390 , 2018-04-12
DGA-master\gui\Read_Config.m, 258 , 2018-04-12
DGA-master\methods, 0 , 2018-04-12
DGA-master\methods\ANN_1.mat, 6411 , 2018-04-12
DGA-master\methods\CPP, 0 , 2018-04-12
DGA-master\methods\CPP\DGA_Test.sln, 961 , 2018-04-12
DGA-master\methods\CPP\DGA_Test.vcxproj, 4100 , 2018-04-12
DGA-master\methods\CPP\DGA_Test.vcxproj.filters, 958 , 2018-04-12
DGA-master\methods\CPP\Release, 0 , 2018-04-12
DGA-master\methods\CPP\Release\DGA_Test.exe, 10240 , 2018-04-12
DGA-master\methods\CPP\Source.cpp, 1050 , 2018-04-12
DGA-master\methods\DGA_ANN.m, 1203 , 2018-04-12
DGA-master\methods\DGA_Cond_Prob.m, 2904 , 2018-04-12
DGA-master\methods\DGA_Duval.m, 1089 , 2018-04-12
DGA-master\methods\DGA_Fortran.EXE, 24854 , 2018-04-12
DGA-master\methods\DGA_IEC.m, 2411 , 2018-04-12
DGA-master\methods\DGA_Key.m, 778 , 2018-04-12
DGA-master\methods\DGA_Roger4.m, 2263 , 2018-04-12
DGA-master\methods\Fortran, 0 , 2018-04-12
DGA-master\methods\Fortran\Build, 0 , 2018-04-12
DGA-master\methods\Fortran\Build\DGA_Clustering.exe, 57130 , 2018-04-12
DGA-master\methods\Fortran\Build\libgcc_s_dw2-1.dll, 113678 , 2018-04-12
DGA-master\methods\Fortran\Build\libgfortran-4.dll, 1883150 , 2018-04-12
DGA-master\methods\Fortran\Build\libquadmath-0.dll, 484878 , 2018-04-12
DGA-master\methods\Fortran\Build\libwinpthread-1.dll, 46592 , 2018-04-12
DGA-master\methods\Fortran\BuildLog, 170 , 2018-04-12
DGA-master\methods\Fortran\DGA_Clustering.f95, 4904 , 2018-04-12
DGA-master\methods\Fortran\build.bat, 134 , 2018-04-12
DGA-master\methods\IEC_REFINNING_NEW.m, 2600 , 2018-04-12
DGA-master\methods\MEAN_STD_PD_AR_TH_D1_D2_T1_T2_T3_323.m, 4543 , 2018-04-12
DGA-master\methods\ROG_REFINNING_NEW.m, 4887 , 2018-04-12
DGA-master\octave-workspace, 2691 , 2018-04-12
DGA-master\octave, 0 , 2018-04-12
DGA-master\octave\movegui.m, 3324 , 2018-04-12
DGA-master\octave\seq.m, 185 , 2018-04-12
DGA-master\res, 0 , 2018-04-12
DGA-master\res\DGA_New.m, 408 , 2018-04-12
DGA-master\res\add-button.jpg, 18888 , 2018-04-12
DGA-master\res\delete-button.jpg, 15800 , 2018-04-12
DGA-master\res\run.png, 3201 , 2018-04-12
DGA-master\utils, 0 , 2018-04-12
DGA-master\utils\LoadButtonImage.m, 361 , 2018-04-12
DGA-master\utils\SetDefaultBackgroundColor.m, 273 , 2018-04-12
DGA-master\utils\relativepath.m, 2986 , 2018-04-12

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0 个回复

  • state_feedback
    使用simulink模型搭建的无人机纵向仿真模型，研究生阶段飞控大作业或许能用到(A vertical UAV simulation model built with simulink ,maybe useful to the graduates flight control lectures large operations )
    2021-03-01 21:49:34下载
    积分：1
  • SinWmk
    余弦波是渐变的，当ωt+φ的取值变化量很小时，Y的变化量也是很微小的。余弦波的这一特性保证了嵌入水印后相邻字符灰度值差别不大，人眼无法识别，从而保证了水印的不可见性和图像质量。(Cosine wave is gradual, and when ωt+ φ values variation is very small, Y amount of change is very small. Cosine waves This feature ensures that watermarked gray value of adjacent character is not very different, the human eye does not recognize, thus ensuring the invisibility of watermark and image quality.)
    2009-11-24 16:29:52下载
    积分：1
  • boostingstyle
    boost升压电路，使用电压外环和电流内环双闭环控制，可以很好地稳定电压，保持功率恒定，采用电流模式(boost boost circuit, using the voltage outer and inner current double closed-loop control, can be a good and stable voltage, keep the power constant current mode)
    2020-12-02 15:49:27下载
    积分：1
  • register
    icp算法源代码，主要用于点云拼合，是基于matlab上编写的,实现两个点云数据的对准(ICP algorithm source code, mainly used for point cloud registration, is prepared based on MATLAB, the realization of the two point cloud data alignment )
    2016-09-23 21:12:42下载
    积分：1
  • 最速下降法
    说明：  最速下降法是迭代法的一种,可以用于求解最小二乘问题(线性和非线性都可以)。在求解机器学习算法的模型参数，即无约束优化问题时，梯度下降（Gradient Descent）是最常采用的方法之一，另一种常用的方法是最小二乘法。在求解损失函数的最小值时，可以通过梯度下降法来一步步的迭代求解，得到最小化的损失函数和模型参数值。反过来，如果我们需要求解损失函数的最大值，这时就需要用梯度上升法来迭代了。在机器学习中，基于基本的梯度下降法发展了两种梯度下降方法，分别为随机梯度下降法和批量梯度下降法。(The steepest descent method is a kind of iterative method, which can be used to solve the least squares problem (both linear and nonlinear). In solving the model parameters of machine learning algorithm, that is, unconstrained optimization, gradient descent is one of the most commonly used methods, and the other is the least square method. When solving the minimum value of loss function, the gradient descent method can be used step by step to get the minimum value of loss function and model parameters. Conversely, if we need to solve the maximum value of the loss function, then we need to use the gradient rise method to iterate. In machine learning, two kinds of ladders are developed based on the basic gradient descent method)
    2019-11-24 13:06:03下载
    积分：1
  • cheby
    本程序实现chebyshev I型低通滤波(the program chebyshev I-low-pass filter)
    2007-05-30 21:58:32下载
    积分：1
  • ssim_index_new
    structural similarity index
    2013-09-25 19:05:46下载
    积分：1
  • GA
    说明：  基于Matlab的GA算法，供算法应用学习者参考(GA algorithm based on Matlab for algorithm application learners Reference)
    2012-08-29 20:06:05下载
    积分：1
  • cljscx
    说明：  一个简单的潮流计算程序，计算其电压电流功率等数据(Simple Answer to Power Flow Computing Program)
    2019-04-08 18:51:58下载
    积分：1
  • matlab入门数据与代码
    说明：  matlab的一些入门代码，包括但不限于输入输出、选择结构、循环结构、函数、画图等(Introduction to MATLAB with some basic code)
    2020-06-30 23:44:47下载
    积分：1
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