登录
首页 » matlab » ruan zhu

ruan zhu

于 2020-07-09 发布
0 232
下载积分: 1 下载次数: 1
我要下载

代码说明：

说明：  一个用DBN做时间序列预测的实例，内包括了数据(An example of using DBN to predict time series includes data)

文件列表：

ruan zhu\choose.fig, 6677 , 2020-07-03
ruan zhu\choose.m, 3556 , 2020-01-11
ruan zhu\contact.fig, 7981 , 2020-07-03
ruan zhu\contact.m, 3495 , 2020-07-03
ruan zhu\cross.m, 306 , 2018-01-12
ruan zhu\DeepLearnToolbox\2019负荷.xlsx, 243570 , 2020-03-27
ruan zhu\DeepLearnToolbox\EMDDBN\AEMO_importdata_one.m, 1068 , 2020-07-03
ruan zhu\DeepLearnToolbox\EMDDBN\AEMO_importdata_two.m, 1068 , 2020-07-03
ruan zhu\DeepLearnToolbox\EMDDBN\Datasets\AEMO_NSW.mat, 68825 , 2014-09-16
ruan zhu\DeepLearnToolbox\EMDDBN\Datasets\AEMO_QLD.mat, 66211 , 2015-01-04
ruan zhu\DeepLearnToolbox\EMDDBN\Datasets\AEMO_SA.mat, 58170 , 2014-09-16
ruan zhu\DeepLearnToolbox\EMDDBN\Datasets\AEMO_TAS.mat, 55411 , 2014-09-16
ruan zhu\DeepLearnToolbox\EMDDBN\Datasets\AEMO_VIC.mat, 67017 , 2015-07-02
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\.travis.yml, 249 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\CAE\caeapplygrads.m, 1219 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\CAE\caebbp.m, 917 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\CAE\caebp.m, 1011 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\CAE\caedown.m, 259 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\CAE\caeexamples.m, 754 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\CAE\caenumgradcheck.m, 3618 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\CAE\caesdlm.m, 845 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\CAE\caetrain.m, 1148 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\CAE\caeup.m, 489 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\CAE\max3d.m, 173 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\CAE\scaesetup.m, 1937 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\CAE\scaetrain.m, 270 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\CNN\cnnapplygrads.m, 575 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\CNN\cnnbp.m, 2141 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\CNN\cnnff.m, 1774 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\CNN\cnnnumgradcheck.m, 3430 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\CNN\cnnsetup.m, 2020 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\CNN\cnntest.m, 193 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\CNN\cnntrain.m, 845 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\CONTRIBUTING.md, 544 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\create_readme.sh, 744 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\data\mnist_uint8.mat, 14735220 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\DBN\dbnsetup.m, 557 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\DBN\dbntrain.m, 232 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\DBN\dbnunfoldtonn.m, 425 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\DBN\rbmdown.m, 90 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\DBN\rbmtrain.m, 1401 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\DBN\rbmup.m, 89 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\LICENSE, 1313 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\NN\nnapplygrads.m, 628 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\NN\nnbp.m, 1638 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\NN\nnchecknumgrad.m, 704 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\NN\nneval.m, 811 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\NN\nnff.m, 1848 , 2019-04-30
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\NN\nnpredict.m, 192 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\NN\nnsetup.m, 1844 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\NN\nntest.m, 184 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\NN\nntrain.m, 2415 , 2019-04-22
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\NN\nnupdatefigures.m, 1858 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\README.md, 8730 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\README_header.md, 2256 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\REFS.md, 950 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\SAE\saesetup.m, 132 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\SAE\saetrain.m, 308 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\tests\runalltests.m, 165 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\tests\test_cnn_gradients_are_numerically_correct.m, 552 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\tests\test_example_CNN.m, 981 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\tests\test_example_DBN.m, 1031 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\tests\test_example_NN.m, 3247 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\tests\test_example_SAE.m, 934 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\tests\test_nn_gradients_are_numerically_correct.m, 749 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\util\allcomb.m, 2618 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\util\expand.m, 1958 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\util\flicker.m, 208 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\util\flipall.m, 80 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\util\fliplrf.m, 543 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\util\flipudf.m, 576 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\util\im2patches.m, 313 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\util\isOctave.m, 108 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\util\makeLMfilters.m, 1895 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\util\myOctaveVersion.m, 169 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\util\normalize.m, 97 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\util\patches2im.m, 242 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\util\randcorr.m, 283 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\util\randp.m, 2083 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\util\rnd.m, 49 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\util\sigm.m, 48 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\util\sigmrnd.m, 126 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\util\softmax.m, 256 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\util\tanh_opt.m, 54 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\util\visualize.m, 1072 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\util\whiten.m, 183 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\deeplearning\util\zscore.m, 137 , 2014-06-26
ruan zhu\DeepLearnToolbox\EMDDBN\EMD_DBN_one.m, 3964 , 2020-07-03
ruan zhu\DeepLearnToolbox\EMDDBN\EMD_DBN_two.m, 3935 , 2020-07-03
ruan zhu\DeepLearnToolbox\EMDDBN\errormeasure.m, 631 , 2017-03-30
ruan zhu\DeepLearnToolbox\EMDDBN\errperf.m, 5289 , 2019-04-22
ruan zhu\DeepLearnToolbox\EMDDBN\nnpredicty.m, 140 , 2014-07-06
ruan zhu\DeepLearnToolbox\EMDDBN\package_emd\bugfix.sh, 216 , 2015-03-11
ruan zhu\DeepLearnToolbox\EMDDBN\package_emd\EMDs\cemdc.m, 2354 , 2015-03-11
ruan zhu\DeepLearnToolbox\EMDDBN\package_emd\EMDs\cemdc2.m, 2362 , 2015-03-11
ruan zhu\DeepLearnToolbox\EMDDBN\package_emd\EMDs\cemdc2_fix.m, 2312 , 2015-03-11
ruan zhu\DeepLearnToolbox\EMDDBN\package_emd\EMDs\cemdc_fix.m, 2305 , 2015-03-11
ruan zhu\DeepLearnToolbox\EMDDBN\package_emd\EMDs\emd.m, 22275 , 2015-03-11
ruan zhu\DeepLearnToolbox\EMDDBN\package_emd\EMDs\emdc.m, 2280 , 2015-03-11
ruan zhu\DeepLearnToolbox\EMDDBN\package_emd\EMDs\emdc_fix.m, 2141 , 2015-03-11

下载说明：请别用迅雷下载，失败请重下，重下不扣分！

发表评论

0 个回复

  • LMSsuanfa
    一种最简单的LMS算法，自适应滤波器中经常采用的算法(LMS MATLAB Adaptive filter)
    2009-05-09 21:12:05下载
    积分：1
  • DCT
    在MATLAB里，基于dct域,实现在二维图像中嵌入数字水印 并提取。(In MATLAB, the dct-based domain, the realization of two-dimensional images in the digital watermark embedding and extraction.)
    2009-05-16 18:07:25下载
    积分：1
  • ploting
    可以实现动态绘制矩形、圆形、椭圆形等图样。matlab例程(Can dynamically draw the rectangle, round, oval and other patterns. matlab routines)
    2010-05-13 08:53:04下载
    积分：1
  • BatchSimulation
    As we know, the energy needed for sensors should be electrical. So the rst step before start to work is to convert environmental energy to electrical one. Inside the node there are dierent components for sensing the area, collecting data and processing the data. So the main dierent part of this kind of nodes, in compare of WSN nodes, is the power part. Once the stored energy reach to a certain amount, the power supply for micro controller and transceiver will start to work. They will continue to work until they have energy, as soon as the energy nish, they stops and energy storage device start to work to save energy again. The other fact should be consider about EH-WSN node is that the available energy can change in dierent nodes. So every node has its own harvesting rate.
    2013-07-20 14:34:56下载
    积分：1
  • Julian-day-toGregorian-calendar
    本程序实现的功能是将儒略日转化为公历年月日(The implementation of the program function is to convert a Julian day to the Gregorian calendar (date) (month) (year) )
    2013-08-22 12:36:48下载
    积分：1
  • wav2aud
    听觉模型的算法实现，分5个步骤，得到听觉谱矩阵。(Auditory model algorithm, in five steps, get auditory spectrum matrix.)
    2011-11-18 10:37:24下载
    积分：1
  • single-phase-rectifier
    单相全波整流模型,实现稳定的波形输出和优化的控制算法(Single phase full wave rectification model, achieving stable waveform output and optimal control algorithm )
    2015-09-19 09:18:10下载
    积分：1
  • bispeci
    与滤波器设计有关的matlab工具箱函数：bispeci.m(And filter design-related functions matlab toolbox: bispeci.m)
    2008-06-24 14:08:53下载
    积分：1
  • Chen_review_PE_wind_200908
    风机发电的文献 非常的详细 有关matlab 的例程都很明确(Fan power is very detailed literature about the matlab routines very clear)
    2014-11-05 13:06:10下载
    积分：1
  • SVM-and-kernel
    用于数据挖掘，在支持向量机中有重要的应用，主要用于做数据分类。(For data mining, have important applications in support vector machine, is mainly used for data classification.)
    2014-12-01 21:24:37下载
    积分：1
  • 696516资源总数
  • 106642会员总数
  • 12今日下载

热门标签

Visual C++ matlab C/C++ Java Others Unix_Linux EasyLanguage易语言 Visual Basic C# PDF C++ Delphi VHDL ASP WINDOWS
热门下载

Copyright© 2019-2026 | Powered by Imdn | 浙ICP备16007336号 | IMDN交流 | 联系客服（QQ：3324461878）

本网站资源收集自网络及网站用户上传，若有侵权请邮件至（3324461878@qq.com），核实后我们会在24小时内删除，谢谢！