登录
首页 » matlab » Bayesian_Wavelet_Network

Bayesian_Wavelet_Network

于 2014-12-11 发布 文件大小:225KB
0 241
下载积分: 1 下载次数: 15

代码说明:

  基于贝叶斯网络的小波阈值的图像的降噪,希望能对你们有一定的用处。(Bayesian network based on wavelet threshold image de-noising, hope to have some helpful to you)

文件列表:

Bayesian-Wavelet-Network
........................\ReadMe.m,1145,2010-09-29
........................\Wavenettool
........................\...........\Bani_Plot.m,1137,2010-09-29
........................\...........\DataNameRead.m,770,2010-09-29
........................\...........\ExportForm_Bani.fig,2179,2007-07-26
........................\...........\ExportForm_Bani.m,6481,2007-07-01
........................\...........\FindCorrectName.m,680,2010-09-29
........................\...........\FindData.m,1116,2010-09-29
........................\...........\FindSelectedData.m,636,2010-09-29
........................\...........\getSlectedvar.m,625,2010-09-29
........................\...........\GetValue.m,463,2010-09-29
........................\...........\hs_err_pid2720.log,12510,2007-07-29
........................\...........\ImportForm_Bani.fig,4830,2007-07-28
........................\...........\ImportForm_Bani.m,10965,2007-07-28
........................\...........\InitialForm_Bani.fig,3504,2007-07-31
........................\...........\InitialForm_Bani.m,9788,2007-07-31
........................\...........\MackeyGlass
........................\...........\...........\Data
........................\...........\...........\....\In_Test.mat,3835,2010-09-25
........................\...........\...........\....\In_Train.mat,2003,2010-09-25
........................\...........\...........\....\Nb_Sample.mat,184,2010-09-25
........................\...........\...........\....\Nb_Test.mat,181,2010-09-25
........................\...........\...........\....\Nb_Train.mat,180,2010-09-25
........................\...........\...........\....\Out_Test.mat,838,2010-09-25
........................\...........\...........\....\Out_Train.mat,523,2010-09-25
........................\...........\...........\....\Pattern.mat,51924,2010-09-25
........................\...........\...........\....\Target.mat,45236,2010-09-25
........................\...........\...........\Mackey.m,284,2010-09-25
........................\...........\...........\MakeyGlass.m,1031,2007-08-19
........................\...........\...........\Normalize.m,545,2007-07-09
........................\...........\MM_In_Out.fig,1507,2007-07-29
........................\...........\MM_In_Out.m,3240,2007-07-29
........................\...........\NewDataForm_Bani.fig,2963,2007-07-29
........................\...........\NewDataForm_Bani.m,7776,2007-07-27
........................\...........\NewNetForm_Bani.fig,4510,2007-07-26
........................\...........\NewNetForm_Bani.m,18263,2007-07-27
........................\...........\Prep2Save.m,936,2010-09-29
........................\...........\ReadSelectedStr.m,439,2010-09-29
........................\...........\Save2Bani.m,519,2010-09-29
........................\...........\SaveResult.m,631,2010-09-29
........................\...........\SimulationForm_Bani.fig,5228,2010-09-25
........................\...........\SimulationForm_Bani.m,11307,2010-09-25
........................\...........\TrainingForm_Bani.fig,4599,2007-07-31
........................\...........\TrainingForm_Bani.m,15291,2010-09-25
........................\...........\ViewForm_Bani.fig,1931,2007-07-01
........................\...........\ViewForm_Bani.m,6900,2007-07-26
........................\...........\WaveletNetwork.fig,6951,2010-09-29
........................\...........\WaveletNetwork.m,17295,2010-09-29
........................\...........\Wavenet
........................\...........\.......\@Wavelet_Network
........................\...........\.......\................\display.m,1061,2010-09-29
........................\...........\.......\................\Initial_Wn_Coef2.m,964,2010-09-29
........................\...........\.......\................\private
........................\...........\.......\................\.......\Wavelon.m,1676,2010-09-29
........................\...........\.......\................\.......\Wavelon_array.m,783,2010-09-29
........................\...........\.......\................\sim.m,727,2010-09-29
........................\...........\.......\................\subsasgn.m,2015,2010-09-29
........................\...........\.......\................\subsref.m,1281,2010-09-29
........................\...........\.......\................\Train_Wn.m,2328,2010-09-29
........................\...........\.......\................\Wavelet_Network.m,3190,2010-09-29
........................\...........\.......\Bani_MexiHat.m,2453,2010-09-29
........................\...........\.......\errbayes.m,1555,2010-09-29
........................\...........\.......\gbayes.m,1781,2010-09-29
........................\...........\.......\HMC.m,9105,2010-09-29
........................\...........\.......\INITWNET.M,8686,2010-09-29
........................\...........\.......\Net_err.m,897,2010-09-29
........................\...........\.......\Net_Grd.m,762,2010-09-29
........................\...........\.......\Net_pack.m,1069,2010-09-29
........................\...........\.......\Net_unpack.m,1374,2010-09-29
........................\...........\.......\PostData.m,266,2010-09-29
........................\...........\.......\PREPDATA.M,1240,2010-09-29
........................\...........\.......\Thumbs.db,20992,2010-09-29
........................\...........\.......\WAVEDEF.M,998,2010-09-29
........................\...........\.......\WAVELON.M,1319,2010-09-29
........................\...........\.......\WavelonOutput.m,584,2010-09-29
........................\...........\.......\WavenetOutput.m,2138,2010-09-29
........................\...........\.......\Wavenet_bkp.m,2078,2010-09-29
........................\...........\.......\Wavenet_err.m,806,2010-09-29
........................\...........\.......\Wavenet_Grd.m,898,2010-09-29
........................\...........\.......\Wavenet_pack.m,892,2010-09-29
........................\...........\.......\Wavenet_unpack.m,1632,2010-09-29
........................\...........\.......\Wn_HMC_Training.m,2567,2010-09-29
........................\...........\.......\Wn_MSE.m,497,2010-09-29
........................\...........\WavenetObject.m,1420,2010-09-29
........................\...........\Wavenettool.m,264,2010-09-29
license.txt,1339,2014-02-12

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

  • asf
    it is matlab source code implemeted in Matlab 7.6. this is utilized in matlab.
    2011-05-21 16:49:30下载
    积分:1
  • PSOPID
    PSO PID 粒子群优化的最优控制器 用于交通流量控制 PSO PID 粒子群优化的最优控制器 用于交通流量控制(Particle Swarm Optimization PSO PID controller for optimal traffic flow control Particle Swarm Optimization PSO PID controller for optimal traffic flow control)
    2021-04-07 20:29:01下载
    积分:1
  • MATLAB_em
    详细的介绍了matlab电子仿真与应用,适合初学者学习与研究(Detailed description of the matlab simulation and electronic applications, suitable for beginners to learn and study)
    2010-06-20 12:11:16下载
    积分:1
  • SimulinkLearning
    《基于MATLAB7.X Simulink Stateflow系统仿真分析及设计》第12章所有的例题及习题程序,用于matlab stateflow的学习(" Based on the simulation analysis and design MATLAB7.X Simulink Stateflow system" Chapter 12, all of the examples and exercises program for learning matlab stateflow)
    2013-01-11 11:51:25下载
    积分:1
  • MATLABxiaobofenxi
    里面是小波分析的实现方法,还有数学介绍。数字信号处理上机实验答案(第三版).docx的电子版(There is a wavelet analysis method, as well as mathematical description. Digital signal processing on the experimental answer (third edition). Electronic version of docx)
    2013-12-16 23:28:08下载
    积分:1
  • logiscoln
    logistics的介绍与MATLAB算法的实现,我相信你一定会找到的。(logistics of the introduction and implementation of MATLAB algorithms, I believe you will find.)
    2010-08-22 09:11:13下载
    积分:1
  • LABEX1
    matlab 数字实验, 关于数字信号处理课程matlab的数字实验。(matlab digital experiments on digital signal processing matlab figures experimental courses.)
    2008-02-29 09:22:43下载
    积分:1
  • cec2006
    演化算法标准测试函数CEC2006,括Matlab代码,JAVA,C代码。(Problem Deˉnitions and Evaluation Criteria for the CEC 2006 Special Session on Constrained Real-Parameter Optimization)
    2020-12-03 16:59:25下载
    积分:1
  • lssvmMATLAB
    最小二乘支持向量机,采用matlab编写,可以直接使用,SVM是经典的分类和预测算法。(Squares support vector machine, using matlab prepared, can be used directly, SVM is the classic classification and prediction algorithms.)
    2015-12-10 18:21:30下载
    积分:1
  • gudaopanduan
    对解列的一个网络,判断出其子系统的个数及每个子系统的节点及其支路(For splitting a network, determine the number of its subsystems and each subsystem node and branch)
    2013-07-15 15:55:58下载
    积分:1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载