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GUI_xtlb_LMD_YL_BYSJ

于 2017-03-02 发布文件大小:13708KB

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下载积分: 1 下载次数: 71

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代码说明：

基于形态滤波和局域值分解的齿轮故障诊断matlabGUI程序(Based on morphology filtering and local matlabGUI value decomposition of gear fault diagnosis process)

文件列表：

GUI_xtlb_LMD_YL_BYSJ
....................\clx_chui.txt,31200000,2012-12-10
....................\clx_chui1.txt,20800000,2012-12-10
....................\clx_heng.txt,31200000,2012-12-10
....................\clx_heng1.txt,20800000,2012-12-10
....................\Crack0Q_0_800_01.txt,290176,2006-01-17
....................\Crack1Q_0_1000_01.txt,292147,2006-01-17
....................\Crack1Q_0_1200_01.txt,291750,2006-01-17
....................\Crack1Q_0_2000_01.txt,291336,2006-01-17
....................\Crack1Q_0_800_01.txt,291297,2006-01-17
....................\Crack2Q_0_800_01.txt,288917,2006-01-17
....................\Crack3Q_0_800_01.txt,286773,2006-01-17
....................\Crack4Q_0_800_01.txt,286720,2006-01-17
....................\DataProcessing.fig,17816,2016-04-06
....................\DataProcessing.m,22044,2016-04-06
....................\LMD.m,7371,2016-04-02
....................\xt.m,3201,2016-04-06
....................\xtlb_LMD.fig,27964,2016-04-07
....................\xtlb_LMD.m,10668,2016-04-07

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T=[1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1] 输入向量的最大值和最小值 threshold=[0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1] net=newff(threshold,[31 3],{ tansig , logsig }, trainlm ) 训练次数为1000，训练目标为0.01，学习速率为0.1 net.trainParam.epochs=1000 net.trainParam.goal=0.01 LP.lr=0.1 net = train(net,P,T) 测试数据，和训练数据不一致 P_test=[0.2101 0.0950 0.1298 0.1359 0.2601 0.1001 0.0753 0.0890 0.0389 0.1451 0.0128 0.1590 0.2452 0.0512 0.1319 0.2593 0.1800 0.0711 0.2801 0.1501 0.1298 0.1001 0.1891 0.2531 0.0875 0.0058 0.1803 0.0992 0.0802 0.1002 (T = [1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1] ' of the maximum and minimum input vector threshold = [0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1] net = newff (threshold, [31 3], {' tansig' , ' logsig' }, ' trainlm' ) training times for the 1000 target of 0.01 training, learning rate of 0.1 net.trainParam.epochs = 1000 net. trainParam.goal = 0.01 LP.lr = 0.1 net = train (net, P, T) test data, and training data inconsistencies P_test = [0.2101 0.0950 0.1298 0.1359 0.2601 0.1001 0.0753 0.0890 0.0389 0.1451 0.0128 0.1590 0.2452 0.0512 0.1319 0.2593 0.1800 0.0711 0.2801 0.1501 0.1298 0.1001 0.1891 0.2531 0.0875 0.0058 0.1803 0.0992 0.0802 0.1002 )

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