登录
首页 » matlab » HOSP-matlab

HOSP-matlab

于 2011-08-16 发布 文件大小:121KB
0 195
下载积分: 1 下载次数: 60

代码说明:

  这是一份高阶统计量的资料,有相关的程序,目前高阶统计量是研究的热门话题,对于地震子波的高精度提取有重要意义。(This is a higher-order statistics of the data full of relevant procedures,and at present higher-order statistics is a hot topic which is very important and significant for high-precision seismic wavelet extraction.)

文件列表:

高阶统计量
..........\hosa
..........\....\ARMAQS.M,4452,2011-06-09
..........\....\ARMARTS.M,2862,2011-06-09
..........\....\ARMASYN.M,4044,2011-06-09
..........\....\ARORDER.M,4165,2011-06-09
..........\....\ARRCEST.M,4034,2011-06-09
..........\....\BICEPS.M,5449,2011-06-09
..........\....\BICEPSF.M,5139,2011-06-09
..........\....\BICOHER.M,4206,2011-06-09
..........\....\BICOHERX.M,4717,2011-06-09
..........\....\BISPECD.M,5854,2011-06-09
..........\....\BISPECDX.M,6508,2011-06-09
..........\....\BISPECI.M,5243,2011-06-09
..........\....\BISPECT.M,1974,2011-06-09
..........\....\bispest.asv,5867,2011-06-09
..........\....\CONTENTS.M,4939,2011-06-09
..........\....\CUM2EST.M,2069,2011-06-09
..........\....\CUM2X.M,3133,2011-06-09
..........\....\CUM3EST.M,2902,2011-06-09
..........\....\CUM3X.M,3611,2011-06-09
..........\....\CUM4EST.M,4463,2011-06-09
..........\....\CUM4X.M,5704,2011-06-09
..........\....\CUMEST.M,2764,2011-06-09
..........\....\CUMTRUE.M,4516,2011-06-09
..........\....\DOA.M,5955,2011-06-09
..........\....\DOAGEN.M,5605,2011-06-09
..........\....\GLSTAT.M,8677,2011-06-09
..........\....\HARMEST.M,6512,2011-06-09
..........\....\HARMGEN.M,3757,2011-06-09
..........\....\HOSAHELP.M,4260,2011-06-09
..........\....\HOSAVER.M,706,2011-06-09
..........\....\HPRONY.M,3194,2011-06-09
..........\....\INFO.XML,402,2011-06-09
..........\....\IVCAL.M,2118,2011-06-09
..........\....\MAEST.M,3374,2011-06-09
..........\....\MAORDER.M,2915,2011-06-09
..........\....\MATUL.M,2840,2011-06-09
..........\....\NLGEN.M,2269,2011-06-09
..........\....\NLPOW.M,3912,2011-06-09
..........\....\NLTICK.M,4611,2011-06-09
..........\....\PICKPEAK.M,2721,2011-06-09
..........\....\QPCGEN.M,5725,2011-06-09
..........\....\QPCTOR.M,4684,2011-06-09
..........\....\README.M,6235,2011-06-09
..........\....\RIVDL.M,5357,2011-06-09
..........\....\RIVTR.M,3031,2011-06-09
..........\....\RPIID.M,2418,2011-06-09
..........\....\TDE.M,4415,2011-06-09
..........\....\TDEB.M,3052,2011-06-09
..........\....\TDEGEN.M,3951,2011-06-09
..........\....\TDER.M,3277,2011-06-09
..........\....\TLS.M,1342,2011-06-09
..........\....\TRENCH.M,2479,2011-06-09
..........\....\TRISPECT.M,2552,2011-06-09
..........\....\WIG2.M,3233,2011-06-09
..........\....\WIG2C.M,3860,2011-06-09
..........\....\WIG3.M,3335,2011-06-09
..........\....\WIG3C.M,4146,2011-06-09
..........\....\WIG4.M,3447,2011-06-09
..........\....\WIG4C.M,4010,2011-06-09
..........\hos_2
..........\.....\arma_gen.m,2304,2011-06-09
..........\.....\calculate_SER.m,2179,2011-06-09
..........\.....\exponential.m,1053,2011-06-09
..........\.....\hos_id.m,1096,2011-06-09
..........\.....\hos_matrix.m,401,2011-06-09
..........\.....\index.m,687,2011-06-09
..........\.....\iterate.m,1526,2011-06-09
..........\.....\MMSE_bse.m,1198,2011-06-09
..........\.....\modulo.m,222,2011-06-09
..........\.....\moments.m,1662,2011-06-09
..........\.....\PAM.m,693,2011-06-09
..........\.....\qam.m,986,2011-06-09
..........\.....\slice_est.m,1281,2011-06-09
..........\.....\Spectrum-est
..........\.....\............\Spectrum-est
..........\.....\............\............\CUM2X.M,3133,2011-06-09
..........\.....\............\............\CUM4X.M,5743,2011-06-09
..........\.....\............\............\estCum1.m,5520,2011-06-09
..........\.....\............\............\estTris4.m,5405,2011-06-09
..........\.....\............\............\g_cc3_matrix.m,2024,2011-06-09
..........\.....\............\............\g_cc4_3D.m,6644,2011-06-09
..........\.....\............\............\true_cum3.m,1301,2011-06-09
..........\.....\............\............\true_cum4_3D.m,1479,2011-06-09
..........\.....\spec_est.m,958,2011-06-09
..........\window_4_order.m,1380,2011-06-09

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

  • homework6
    说明:  使用matlab实现手写阿拉伯数字的识别。(Using matlab to achieve the recognition of handwritten Arabic numerals.)
    2008-11-21 18:00:02下载
    积分:1
  • Byte-Compress
    Bytecompression detection using run length encoding
    2015-02-21 18:30:03下载
    积分:1
  • suanfa
    说明:  naga2算法的matlab实现,可以实现多目标优化,希望对大家有所帮助,测试函数改了一些,但是可以正常运行(Naga2 algorithm matlab implementation, can achieve multi-objective optimization, I hope to help you, test function changed some, but can run normally)
    2020-09-06 10:45:19下载
    积分:1
  • idfig02
    idfig function for wavelet analysis
    2013-05-15 03:32:59下载
    积分:1
  • Particle-Swarm-Algorithm
    粒子群算法,可以自己确定惯性系数的函数式,扩展性良好,可以实现多维(Particle Swarm Algorithm)
    2017-04-30 16:50:51下载
    积分:1
  • 041715201036438
    说明:  短波信道watterson模型的建模仿真,已经编译通过。(The simulation of Watterson model of HF channel has been compiled.)
    2021-04-23 22:28:48下载
    积分:1
  • SM-Johnson
    It is a SM johnson filter that is using filter designing concept
    2013-11-24 19:55:24下载
    积分:1
  • signals-quick-blind-detection
    介绍一种有关卫星通信信号的快速盲检测方法(satellite communication signals quick blind detection)
    2014-01-19 20:13:16下载
    积分:1
  • adaboost
    AdaBoost元算法属于boosting系统融合方法中最流行的一种,说白了就是一种串行训练并且最后加权累加的系统融合方法。 具体的流程是:每一个训练样例都赋予相同的权重,并且权重满足归一化,经过第一个分类器分类之后, 计算第一个分类器的权重alpha值,并且更新每一个训练样例的权重,然后再进行第二个分类器的训练,相同的方法....... 直到错误率为0或者达到指定的训练轮数,其中最后预测的标签计算是各系统*alpha的加权和,然后sign(预测值)。 可以看出,训练流程是串行的,并且训练样例的权重是一直在变化的,分错的样本的权重不断加大,正确的样本的权重不断减小。 AdaBoost元算法是boosting中流行的一种,还有其他的系统融合的方法,比如bagging方法以及随机森林。 对于非均衡样本的处理,一般可以通过欠抽样(undersampling)或者过抽样(oversampling),欠抽样是削减样本的数目, 过抽样是重复的选取某些样本,最好的方法是两种进行结合的方法。 同时可以通过删除离决策边界比较远的样例。 (AdaBoost boosting systems dollar fusion algorithm is the most popular one, it plainly systems integration approach is a serial train and final weighted cumulative. Specific process is: Each training example is given equal weight, and the weights satisfy normalization, after the first classifiers after Calculating a first classifier weights alpha value for each sample and updates right weight training, and then the second classifier training, the same way ....... 0, or until the specified error rate training rounds, wherein the label is the calculation of the final prediction system* alpha weighted and then sign (predicted value). As can be seen, the training process is serial, and weight training examples is always changing, the right of the wrong sample weight continued to increase, the right to correct sample weight decreasing. AdaBoost algorithm is an element, as well as other methods of boosting popular systems integration, such as bagging and random forest method. For )
    2014-07-09 19:24:29下载
    积分:1
  • xianxinghuigui
    对数据的分析,特别是线性回归其中有好几种方法来进行!(Analysis of the data, in particular linear regression in which there are several ways to carry out!)
    2010-09-01 19:21:54下载
    积分:1
  • 696516资源总数
  • 106668会员总数
  • 21今日下载