​时序信号预测的EMDDBN所有程序我要分享

Timing signal to predict EMDDBN all the procedures

EMDDBN 时序预测 预测

关注次数: 392

下载次数: 1

文件大小: 14801KB

代码分类: 信号处理

开发平台: matlab

下载需要积分: 1积分

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代码描述

中文说明:

EMDDBN所有程序,用于进行对时序信号的预测,里面包括了测试所用的数据


English Description:

EMDDBN all the procedures, used for prediction of timing signals, including the data used in the test


代码预览

EMDDBN\AEMO_importdata_hah.m

EMDDBN\AEMO_importdata_one.m

EMDDBN\Datasets\AEMO_NSW.mat

EMDDBN\Datasets\AEMO_QLD.mat

EMDDBN\Datasets\AEMO_SA.mat

EMDDBN\Datasets\AEMO_TAS.mat

EMDDBN\Datasets\AEMO_VIC.mat

EMDDBN\deeplearning\.travis.yml

EMDDBN\deeplearning\CAE\caeapplygrads.m

EMDDBN\deeplearning\CAE\caebbp.m

EMDDBN\deeplearning\CAE\caebp.m

EMDDBN\deeplearning\CAE\caedown.m

EMDDBN\deeplearning\CAE\caeexamples.m

EMDDBN\deeplearning\CAE\caenumgradcheck.m

EMDDBN\deeplearning\CAE\caesdlm.m

EMDDBN\deeplearning\CAE\caetrain.m

EMDDBN\deeplearning\CAE\caeup.m

EMDDBN\deeplearning\CAE\max3d.m

EMDDBN\deeplearning\CAE\scaesetup.m

EMDDBN\deeplearning\CAE\scaetrain.m

EMDDBN\deeplearning\CNN\cnnapplygrads.m

EMDDBN\deeplearning\CNN\cnnbp.m

EMDDBN\deeplearning\CNN\cnnff.m

EMDDBN\deeplearning\CNN\cnnnumgradcheck.m

EMDDBN\deeplearning\CNN\cnnsetup.m

EMDDBN\deeplearning\CNN\cnntest.m

EMDDBN\deeplearning\CNN\cnntrain.m

EMDDBN\deeplearning\CONTRIBUTING.md

EMDDBN\deeplearning\create_readme.sh

EMDDBN\deeplearning\data\mnist_uint8.mat

EMDDBN\deeplearning\DBN\dbnsetup.m

EMDDBN\deeplearning\DBN\dbntrain.m

EMDDBN\deeplearning\DBN\dbnunfoldtonn.m

EMDDBN\deeplearning\DBN\rbmdown.m

EMDDBN\deeplearning\DBN\rbmtrain.m

EMDDBN\deeplearning\DBN\rbmup.m

EMDDBN\deeplearning\LICENSE

EMDDBN\deeplearning\NN\nnapplygrads.m

EMDDBN\deeplearning\NN\nnbp.m

EMDDBN\deeplearning\NN\nnchecknumgrad.m

EMDDBN\deeplearning\NN\nneval.m

EMDDBN\deeplearning\NN\nnff.m

EMDDBN\deeplearning\NN\nnpredict.m

EMDDBN\deeplearning\NN\nnsetup.m

EMDDBN\deeplearning\NN\nntest.m

EMDDBN\deeplearning\NN\nntrain.m

EMDDBN\deeplearning\NN\nnupdatefigures.m

EMDDBN\deeplearning\README.md

EMDDBN\deeplearning\README_header.md

EMDDBN\deeplearning\REFS.md

EMDDBN\deeplearning\SAE\saesetup.m

EMDDBN\deeplearning\SAE\saetrain.m

EMDDBN\deeplearning\tests\runalltests.m

EMDDBN\deeplearning\tests\test_cnn_gradients_are_numerically_correct.m

EMDDBN\deeplearning\tests\test_example_CNN.m

EMDDBN\deeplearning\tests\test_example_DBN.m

EMDDBN\deeplearning\tests\test_example_NN.m

EMDDBN\deeplearning\tests\test_example_SAE.m

EMDDBN\deeplearning\tests\test_nn_gradients_are_numerically_correct.m

EMDDBN\deeplearning\util\allcomb.m

EMDDBN\deeplearning\util\expand.m

EMDDBN\deeplearning\util\flicker.m

EMDDBN\deeplearning\util\flipall.m

EMDDBN\deeplearning\util\fliplrf.m

EMDDBN\deeplearning\util\flipudf.m

EMDDBN\deeplearning\util\im2patches.m

EMDDBN\deeplearning\util\isOctave.m

EMDDBN\deeplearning\util\makeLMfilters.m

EMDDBN\deeplearning\util\myOctaveVersion.m

EMDDBN\deeplearning\util\normalize.m

EMDDBN\deeplearning\util\patches2im.m

EMDDBN\deeplearning\util\randcorr.m

EMDDBN\deeplearning\util\randp.m

EMDDBN\deeplearning\util\rnd.m

EMDDBN\deeplearning\util\sigm.m

EMDDBN\deeplearning\util\sigmrnd.m

EMDDBN\deeplearning\util\softmax.m

EMDDBN\deeplearning\util\tanh_opt.m

EMDDBN\deeplearning\util\visualize.m

EMDDBN\deeplearning\util\whiten.m

EMDDBN\deeplearning\util\zscore.m

EMDDBN\EMD_DBN_hah.m

EMDDBN\EMD_DBN_one.m

EMDDBN\errormeasure.m

EMDDBN\errperf.m

EMDDBN\nnpredicty.m

EMDDBN\package_emd\bugfix.sh

EMDDBN\package_emd\EMDs\cemdc.m

EMDDBN\package_emd\EMDs\cemdc2.m

EMDDBN\package_emd\EMDs\cemdc2_fix.m

EMDDBN\package_emd\EMDs\cemdc_fix.m

EMDDBN\package_emd\EMDs\emd.m

EMDDBN\package_emd\EMDs\emdc.m

EMDDBN\package_emd\EMDs\emdc_fix.m

EMDDBN\package_emd\EMDs\emd_local.m

EMDDBN\package_emd\EMDs\emd_online.m

EMDDBN\package_emd\EMDs\make_emdc.m

EMDDBN\package_emd\EMDs\src\cemdc.c

EMDDBN\package_emd\EMDs\src\cemdc2.c

EMDDBN\package_emd\EMDs\src\cemdc2_fix.c