登录
首页 » matlab » GNN_1_1b

GNN_1_1b

于 2020-07-03 发布 文件大小:473KB
0 281
下载积分: 1 下载次数: 18

代码说明:

  基于GNN神经网络的系统仿真,具有较好的仿真效果。(Based on GNN neural network system simulation, has a good simulation results.)

文件列表:

GNN_1_1b
........\GNN 1.1b
........\........\comparisonNet
........\........\.............\initializeComparisonNet.m,1962,2007-12-15
........\........\.............\learnComparisonNet.m,5195,2007-12-15
........\........\.............\plotComparisonNetTrainingResults.m,613,2007-12-15
........\........\.............\testComparisonNet.m,7469,2007-12-15
........\........\Configure.m,39956,2011-03-01
........\........\datasets
........\........\........\CliqueDataset.config,1039,2007-12-15
........\........\........\HalfHotDataset.config,892,2007-12-15
........\........\........\makeCliqueDataset.m,9487,2007-12-15
........\........\........\makeCliqueDataset_fix_rand.m,9786,2007-12-15
........\........\........\makeGeneralDataset.m,8650,2007-12-15
........\........\........\makeHalfHotDataset.m,11774,2007-12-15
........\........\........\makeMutagenicDataset.m,13115,2013-02-11
........\........\........\makeNeighborsDataset.m,9303,2007-12-15
........\........\........\makeOddEvenDataset.m,7952,2007-12-15
........\........\........\makeParityDataset.m,7941,2007-12-15
........\........\........\makeSecondOrderNeighborsDataset.m,9856,2007-12-15
........\........\........\makeSubGraphDataset.m,11308,2007-12-15
........\........\........\makeTmpDataset.m,1891,2007-12-15
........\........\........\makeTreeDepthDataset.m,9592,2007-12-15
........\........\........\makeWebPagesScoringDataset.m,10743,2007-12-15
........\........\........\muta-f.pl,390315,2007-12-15
........\........\........\muta-u.pl,77086,2007-12-15
........\........\........\muta.pl,467401,2007-12-15
........\........\........\muta.pl_new,357429,2007-12-15
........\........\........\NeighborsDataset.config,854,2007-12-15
........\........\........\ParityDataset.config,806,2007-12-15
........\........\........\SecondOrderNeighborsDataset.config,875,2007-12-15
........\........\........\SubGraphMatchingDataset.config,1120,2007-12-15
........\........\........\TreeDepthDataset.config,788,2007-12-15
........\........\........\TreeDepthDataset.config.backup,808,2007-12-15
........\........\........\WebPagesScoringDataset.config,868,2007-12-15
........\........\experiments
........\........\...........\init_class.m,432,2007-12-15
........\........\...........\save_learning.m,1046,2007-12-15
........\........\...........\start_learning.m,161,2007-12-15
........\........\...........\test_learning.m,1600,2007-12-15
........\........\GNN.config,2857,2011-03-01
........\........\GNN_DOC.pdf,190123,2011-07-21
........\........\initialization
........\........\..............\initializeNet.m,1547,2007-12-15
........\........\isomorphism
........\........\...........\areIsomorph.m,1664,2007-12-15
........\........\...........\isomorphism.mat,4295,2007-12-15
........\........\...........\mygetPR.m,282,2007-12-15
........\........\...........\mytest.m,287,2007-12-15
........\........\learn.m,552,2007-12-15
........\........\MLP
........\........\...\initializeMLP.m,1653,2007-12-15
........\........\...\testMLP.m,2656,2007-12-15
........\........\...\trainMLP.m,4954,2007-12-15
........\........\neuralNetworks
........\........\..............\backwardOneLayerLinearOutNet.m,431,2007-12-15
........\........\..............\backwardOneLayerNet.m,826,2007-12-15
........\........\..............\backwardTwoLayerLinearOutNet.m,725,2007-12-15
........\........\..............\backwardTwoLayerNet.m,982,2007-12-15
........\........\..............\forwardJacobianOneLayerLinearOutNet.m,166,2007-12-15
........\........\..............\forwardJacobianOneLayerNet.m,239,2007-12-15
........\........\..............\forwardOneLayerLinearOutNet.m,698,2007-12-15
........\........\..............\forwardOneLayerNet.m,340,2007-12-15
........\........\..............\forwardTwoLayerLinearOutNet.m,1207,2007-12-15
........\........\..............\forwardTwoLayerNet.m,1216,2007-12-15
........\........\..............\getDeltaJacobianOneLayerLinearOutNet.m,368,2007-12-15
........\........\..............\getDeltaJacobianOneLayerNet.m,521,2007-12-15
........\........\..............\getDeltaJacobianTwoLayerLinearOutNet.m,831,2007-12-15
........\........\..............\getDeltaJacobianTwoLayerNet.m,929,2007-12-15
........\........\..............\getJacobianOneLayerLinearOutNet.m,781,2007-12-15
........\........\..............\getJacobianTwoLayerLinearOutNet.m,780,2007-12-15
........\........\private
........\........\.......\displayTestRes.m,2271,2007-12-15
........\........\.......\evaluateAccuracyOnGraphs.m,652,2007-12-15
........\........\.......\learn_.m,20232,2007-12-15
........\........\.......\test4autoassociator.m,14720,2007-12-15
........\........\.......\test4autoassociator_old.m,17292,2007-12-15
........\........\.......\test4autoassociator_veryold.m,11827,2007-12-15
........\........\.......\test4classification.m,12806,2011-03-11
........\........\.......\test4regression.m,9993,2011-03-11
........\........\.......\test4uniform.m,5827,2007-12-15
........\........\save_experiment.m,376,2007-12-15
........\........\startSession.m,72,2011-03-01
........\........\systemModels
........\........\............\autoassociatorComputeDeltaError.m,637,2007-12-15
........\........\............\autoassociatorComputeError.m,1456,2007-12-15
........\........\............\linearModelInitialize.m,1128,2007-12-15
........\........\............\linearModelRunBackward.m,3807,2007-12-15
........\........\............\linearModelRunForward.m,2208,2007-12-15
........\........\............\mseComputeDeltaError.m,654,2007-12-15
........\........\............\mseComputeError.m,1344,2007-12-15
........\........\............\neuralModelAutomorphComputeError.m,1651,2007-12-15
........\........\............\neuralModelGetDeltaJacobian.m,1265,2007-12-15
........\........\............\neuralModelGetJacobian.m,1837,2007-12-15
........\........\............\neuralModelInitialize.m,6488,2007-12-15
........\........\............\neuralModelQuadraticComputeDeltaError.m,587,2007-12-15
........\........\............\neuralModelQuadraticComputeError.m,1063,2007-12-15
........\........\............\neuralModelRunBackward.m,1285,2007-12-15
........\........\............\neuralModelRunForward.m,1555,2007-12-15
........\........\............\neuralModelWithProductComputeDeltaError.m,1308,2007-12-15

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

  • DataAccessServer
    OPC DA Server 2.0的实现,实现了异步及订阅的方式(The implementation of OPC DA Server 2 implements asynchronous and subscription methods)
    2017-11-22 17:31:46下载
    积分:1
  • Python网络数据采集
    说明:  python网络数据采集 高清pdf 特别推荐这本书,通俗易懂,作者讲的非常详细(Python Network Data Acquisition high-definition pdf)
    2019-03-10 19:56:25下载
    积分:1
  • m-u0
    计算钢筋混凝土梁弯矩曲率关系,并输出钢筋混凝土梁弯矩关系图(calucate the M-u relationship of reinforecd concrete structure)
    2020-07-13 15:08:51下载
    积分:1
  • ADC_Dma
    说明:  example for lpc1788 adc dma
    2020-04-01 00:26:09下载
    积分:1
  • python的学习笔记 很赞的说,深入浅出,我爱python
    python的学习笔记 很赞的说,深入浅出,我爱python-learning python is Chan notes that in layman
    2022-12-19 17:40:04下载
    积分:1
  • 二维经验模式分解,速度较快,不错的代码,希望对学习HHT的朋友有用...
    二维经验模式分解,速度较快,不错的代码,希望对学习HHT的朋友有用-Two-dimensional empirical mode decomposition, fast, good code
    2022-07-13 08:09:23下载
    积分:1
  • Java华容道源码
    Java华容道源码,代码中有注释,对学习有帮助。以前在手机里运行过。
    2022-07-26 07:30:32下载
    积分:1
  • 《圣剑英雄传
    This an example of an email program . It uses the SMTP Component. To use, you must connect to a SMTP server. Most SMTP servers do not require an account.
    2023-01-20 08:35:04下载
    积分:1
  • LLE
    说明:  数据降维处理算法,流形学习方法。一般用在脸数据集。数据降维处理算法,流形学习方法。一般用在脸数据集。(Data dimension reduction processing algorithm,manifold learning method.Generally used in face data sets.)
    2020-09-30 21:30:10下载
    积分:1
  • VC++.net 时尚编程20例的源代码,对于初学者很有帮助
    VC++.net 时尚编程20例的源代码,对于初学者很有帮助
    2022-02-22 05:05:55下载
    积分:1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载