登录
首页 » matlab » SHMFunctions

SHMFunctions

于 2016-04-18 发布 文件大小:325KB
0 194
下载积分: 1 下载次数: 18

代码说明:

  美 Los Alamos实验室 结构健康监测SHM MATLAB工具包(Structural Health MOnitoring (SHM) MATLAB toolbox)

文件列表:

SHMFunctions
............\Auxiliary
............\.........\Plotting
............\.........\........\labelPlot_shm.m,1755,2014-05-31
............\.........\SensorSupport
............\.........\.............\OptimalSensorPlacement
............\.........\.............\......................\Geometry
............\.........\.............\......................\........\addResp2Geom_shm.m,2101,2014-05-31
............\.........\.............\......................\........\getElementCentroids_shm.m,1008,2014-05-31
............\.........\.............\......................\........\getSensorLayout_shm.m,1958,2014-05-31
............\.........\.............\......................\........\nodeElementPlot_shm.m,3859,2014-05-31
............\.........\.............\......................\........\responseInterp_shm.m,3689,2014-05-31
............\.........\.............\......................\OSP_FisherInfoEIV_shm.m,3848,2014-05-31
............\.........\.............\......................\OSP_MaxNorm_shm.m,5815,2014-05-31
............\.........\.............\SensorDiagnostic
............\.........\.............\................\sdAutoclassify_shm.m,5775,2014-05-31
............\.........\.............\................\sdFeature_shm.m,2648,2014-05-31
............\.........\.............\................\sdPlot_shm.m,5023,2014-05-31
............\DataAcquisition
............\...............\bandLimWhiteNoise_shm.m,1606,2014-05-31
............\...............\buildPairList_shm.m,1965,2014-05-31
............\...............\getGausModSin_shm.m,1929,2014-05-31
............\...............\NationalInstrumentsHighSpeed
............\...............\............................\niFgen.mdd,216037,2011-01-05
............\...............\............................\niScope.mdd,145746,2011-01-05
............\...............\............................\niSwitch.mdd,89139,2011-01-05
............\...............\............................\niTclk.mdd,1941,2011-01-05
............\...............\............................\NI_FGEN_InitConfig_shm.m,1816,2014-05-31
............\...............\............................\NI_FGEN_PrepWave_shm.m,1682,2014-05-31
............\...............\............................\NI_FGEN_SetOptions_shm.m,1929,2014-05-31
............\...............\............................\NI_multiplexSession_shm.m,3306,2014-05-31
............\...............\............................\NI_SCOPE_FetchWaves_shm.m,1670,2014-05-31
............\...............\............................\NI_SCOPE_InitConfig_shm.m,2190,2014-05-31
............\...............\............................\NI_SCOPE_SetOptions_shm.m,2209,2014-05-31
............\...............\............................\NI_SWITCH_Connect_shm.m,2432,2014-05-31
............\...............\............................\NI_SWITCH_Init_shm.m,1002,2014-05-31
............\...............\............................\NI_TCLK_SyncPrep_shm.m,2145,2014-05-31
............\...............\............................\NI_TCLK_Trigger_shm.m,770,2014-05-31
............\...............\splitData_shm.m,1826,2014-05-31
............\...............\Traditional
............\...............\...........\exciteAndAquire_shm.m,4002,2014-05-31
............\FeatureClassification
............\.....................\getThresholdChi2_shm.m,871,2014-05-31
............\.....................\OutlierDetection
............\.....................\................\AssembledDetectors
............\.....................\................\..................\Templates
............\.....................\................\..................\.........\trainBegin.txt,1921,2011-01-05
............\.....................\................\..................\.........\trainEnd.txt,1511,2011-01-05
............\.....................\................\..................\.........\trainMid.txt,449,2011-01-05
............\.....................\................\assembleOutlierDetector_shm.m,14193,2014-05-31
............\.....................\................\detectOutlier_shm.m,4774,2014-05-31
............\.....................\................\NonParametricDetectors
............\.....................\................\......................\FastMetricKernelEstimation
............\.....................\................\......................\..........................\buildCoverTree_shm.m,4739,2014-05-31
............\.....................\................\......................\..........................\DistanceMetrics
............\.....................\................\......................\..........................\...............\l2Dist_shm.m,1086,2014-05-31
............\.....................\................\......................\..........................\...............\lkDist_shm.m,1072,2014-05-31
............\.....................\................\......................\..........................\fastMetricKernelDensity_shm.m,4216,2014-05-31
............\.....................\................\......................\..........................\metricKernel_shm.m,1664,2014-05-31
............\.....................\................\......................\Kernels
............\.....................\................\......................\.......\cosineKernel_shm.m,1060,2014-05-31
............\.....................\................\......................\.......\epanechnikovKernel_shm.m,1069,2014-05-31
............\.....................\................\......................\.......\gaussianKernel_shm.m,1028,2014-05-31
............\.....................\................\......................\.......\quarticKernel_shm.m,1059,2014-05-31
............\.....................\................\......................\.......\triangleKernel_shm.m,1045,2014-05-31
............\.....................\................\......................\.......\triweightKernel_shm.m,1066,2014-05-31
............\.....................\................\......................\.......\uniformKernel_shm.m,1017,2014-05-31
............\.....................\................\......................\learnFastMetricKernelDensity_shm.m,3790,2014-05-31
............\.....................\................\......................\learnKernelDensity_shm.m,3155,2014-05-31
............\.....................\................\......................\learnNLPCA_shm.m,3955,2014-05-31
............\.....................\................\......................\scoreFastMetricKernelDensity_shm.m,1812,2014-05-31
............\.....................\................\......................\scoreKernelDensity_shm.m,1825,2014-05-31
............\.....................\................\......................\scoreNLPCA_shm.m,2545,2014-05-31
............\.....................\................\ParametricDetectors
............\.....................\................\...................\learnFactorAnalysis_shm.m,3903,2014-05-31
............\.....................\................\...................\learnMahalanobis_shm.m,1337,2014-05-31
............\.....................\................\...................\learnPCA_shm.m,2776,2014-05-31
............\.....................\................\...................\learnSVD_shm.m,2761,2014-05-31
............\.....................\................\...................\scoreFactorAnalysis_shm.m,3921,2014-05-31
............\.....................\................\...................\scoreMahalanobis_shm.m,1798,2014-05-31
............\.....................\................\...................\scorePCA_shm.m,2077,2014-05-31
............\.....................\................\...................\scoreSVD_shm.m,3095,2014-05-31
............\.....................\................\SAVEDIR
............\.....................\................\SemiParametricDetectors
............\.....................\................\.......................\learnGMMSemiParametricModel_shm.m,1348,2014-05-31
............\.....................\................\.......................\PartitioningAlgorithms
............\.....................\................\.......................\......................\kdTree_shm.m,3399,2014-05-31
............\.....................\................\.......................\......................\kMeans_shm.m,1261,2014-05-31
............\.....................\................\.......................\......................\kMedians_shm.m,1974,2014-05-31
............\.....................\................\.......................\......................\pdTree_shm.m,3044,2014-05-31
............\.....................\................\.......................\......................\rpTree_shm.m,2825,2014-05-31
............\.....................\................\.......................\scoreGMMSemiParametricModel_shm.m,1373,2014-05-31
............\.....................\................\.......................\Utilities
............\.....................\................\.......................\.........\learnGMM_shm.m,2195,2014-05-31
............\.....................\................\.......................\.........\scoreGMM_shm.m,1706,2014-05-31
............\.....................\................\trainOutlierDetector_shm.m,4320,2014-05-31
............\.....................\................\UseCaseWrappers
............\.....................\................\...............\detectorMultiSiteWrapper_shm.m,6291,2014-05-31
............\.....................\PCA_shm.m,3025,2014-05-31
............\.....................\plotROC_shm.m,4187,2014-05-31

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

  • LMSagl
    设计Matlab程序实现最小均方误差算法,要求:已知模式类的模式向量,依据最小均方误差算法设计判决器(Matlab program designed to achieve the minimum mean square error algorithm requires: pattern vectors known patterns and the like, according to the minimum mean square error algorithm design decider)
    2014-12-10 14:37:40下载
    积分:1
  • WCDMAsim
    一个利用matlab,仿真实现WCDMA的程序:)(A use of matlab, simulation procedures WCDMA realize:))
    2007-08-10 16:15:35下载
    积分:1
  • @dagsvm
    有向无环图支持向量(DAG-SVMS)多类分类方法,是一种新的多类分类方法。该方法采用了最小超球体类包含作为层次分类依据。试验结果表明,采用该方法进行多类分类,跟已有的分类方法相比有更高的分类精度。 (Directed acyclic graph support vector (DAG-SVMS) multi-category classification methods, is a new multi-category classification methods. The method uses the smallest category of super-sphere that contains the level of classification as a basis. The experimental results show that using the method of multiclass classification with the classification method has been compared to a higher classification accuracy.)
    2008-01-08 21:26:29下载
    积分:1
  • DeMat
    差分进化算法标准版,应用时可以自行修改,可以达到较好的优化效果(Differential evolution algorithm standard edition,you can change it if you want to use if for self application Can achieve better optimization effect)
    2015-01-15 20:23:09下载
    积分:1
  • PID
    PID调节器,可以通过调节PID参数观察曲线变化(PID controller, you can adjust the PID parameters observed curve)
    2013-08-16 16:52:47下载
    积分:1
  • IEEE802.15.3aUWB
    IEEE 802.15.3a UWB信道模型仿真,(IEEE 802.15.3a UWB channel model simulation,)
    2008-05-26 16:16:31下载
    积分:1
  • matlabsimulationtoolbox
    matlab仿真工具箱及其相关代码讲义 自己整理的资料(matlab simulation toolbox and associated data compiled code handouts own)
    2010-08-30 15:37:51下载
    积分:1
  • MUSIC_DOA_s
    在未知方向发射信号,由接收阵元接收。对接收的信号使用MUSIC算法处理,得到发射信号方位(process the signals received using MUSIC to get the DOA)
    2009-10-23 14:27:17下载
    积分:1
  • matlab
    学习matlab的好资料,是你学习的好助手,你可以用来学习matlab和数学建模(this is a learn matlab very good )
    2013-08-23 17:28:53下载
    积分:1
  • fisher-recognization
    fisher判别法 本实验通过编制程序体会Fisher线性判别的基本思路,理解线性判别的基本思想,掌握Fisher线性判别问题的实质。 (fisher discriminant method of the present experiment with basic programming experience Fisher linear discriminant ideas, understand the basic idea of ​ ​ a linear discriminant, master Fisher linear discriminant substantive issues.)
    2015-01-17 20:47:22下载
    积分:1
  • 696516资源总数
  • 106918会员总数
  • 4今日下载