文件名称:CNN 11 tezhengtiqu
介绍说明－－下载内容均来自于网络，请自行研究使用
卷积神经网络，用于故障诊断，取得很好的识别效果(Convolution neural network is used for fault diagnosis and achieves good recognition results.)
(系统自动生成,下载前可以参看下载内容)
下载文件列表
| 文件名 | 大小 | 更新时间 |
|---|---|---|
| CNN 11 tezhengtiqu\allcomb.m | 2618 | 2015-12-01 |
| CNN 11 tezhengtiqu\bishedata_jiang.mat | 24907516 | 2018-04-12 |
| CNN 11 tezhengtiqu\cnnapplygrads.m | 904 | 2018-04-11 |
| CNN 11 tezhengtiqu\cnnbp.m | 4317 | 2018-04-11 |
| CNN 11 tezhengtiqu\cnnff.m | 3421 | 2018-04-16 |
| CNN 11 tezhengtiqu\cnnnumgradcheck.m | 3430 | 2015-12-01 |
| CNN 11 tezhengtiqu\cnnsetup.m | 3087 | 2018-04-16 |
| CNN 11 tezhengtiqu\cnntest.m | 257 | 2018-04-18 |
| CNN 11 tezhengtiqu\cnntrain.m | 2599 | 2018-04-29 |
| CNN 11 tezhengtiqu\data_curve.m | 778 | 2018-04-25 |
| CNN 11 tezhengtiqu\divide_TrainTest.m | 609 | 2018-04-19 |
| CNN 11 tezhengtiqu\envelope_spectrum.m | 818 | 2018-04-16 |
| CNN 11 tezhengtiqu\expand.m | 1958 | 2015-12-01 |
| CNN 11 tezhengtiqu\feature_envelope_extraction.m | 888 | 2018-04-16 |
| CNN 11 tezhengtiqu\feature_extraction.m | 897 | 2018-04-19 |
| CNN 11 tezhengtiqu\feature_frequency_extraction.m | 904 | 2018-04-16 |
| CNN 11 tezhengtiqu\feature_main.m | 1680 | 2018-04-17 |
| CNN 11 tezhengtiqu\feature_process_CNN.m | 2564 | 2018-04-27 |
| CNN 11 tezhengtiqu\feature_time_extraction.m | 2490 | 2018-04-15 |
| CNN 11 tezhengtiqu\flicker.m | 208 | 2015-12-01 |
| CNN 11 tezhengtiqu\flipall.m | 93 | 2018-04-11 |
| CNN 11 tezhengtiqu\fliplrf.m | 543 | 2015-12-01 |
| CNN 11 tezhengtiqu\flipudf.m | 576 | 2015-12-01 |
| CNN 11 tezhengtiqu\frequency_spectrum.m | 421 | 2018-04-16 |
| CNN 11 tezhengtiqu\gear_fault_featuredata.mat | 448941 | 2018-04-30 |
| CNN 11 tezhengtiqu\gear_fault_featuredata_jiang.mat | 298975 | 2018-04-30 |
| CNN 11 tezhengtiqu\im2patches.m | 313 | 2015-12-01 |
| CNN 11 tezhengtiqu\isOctave.m | 108 | 2015-12-01 |
| CNN 11 tezhengtiqu\makeLMfilters.m | 1895 | 2015-12-01 |
| CNN 11 tezhengtiqu\mnist_uint8.mat | 14735220 | 2015-12-01 |
| CNN 11 tezhengtiqu\myOctaveVersion.m | 169 | 2015-12-01 |
| CNN 11 tezhengtiqu\normalize.m | 97 | 2015-12-01 |
| CNN 11 tezhengtiqu\patches2im.m | 242 | 2015-12-01 |
| CNN 11 tezhengtiqu\randcorr.m | 283 | 2015-12-01 |
| CNN 11 tezhengtiqu\randp.m | 2083 | 2015-12-01 |
| CNN 11 tezhengtiqu\rnd.m | 49 | 2015-12-01 |
| CNN 11 tezhengtiqu\sigm.m | 48 | 2015-12-01 |
| CNN 11 tezhengtiqu\sigmrnd.m | 126 | 2015-12-01 |
| CNN 11 tezhengtiqu\softmax.m | 256 | 2015-12-01 |
| CNN 11 tezhengtiqu\tanh_opt.m | 54 | 2015-12-01 |
| CNN 11 tezhengtiqu\testing_accuracy.tif | 51533 | 2018-04-30 |
| CNN 11 tezhengtiqu\testing_error.tif | 49093 | 2018-04-30 |
| CNN 11 tezhengtiqu\training_accuracy.tif | 51347 | 2018-04-30 |
| CNN 11 tezhengtiqu\training_error.tif | 48795 | 2018-04-30 |
| CNN 11 tezhengtiqu\visualize.m | 1072 | 2015-12-01 |
| CNN 11 tezhengtiqu\whiten.m | 183 | 2015-12-01 |
| CNN 11 tezhengtiqu\zscore.m | 137 | 2015-12-01 |