登录
首页 » matlab » pls

pls

于 2008-12-10 发布 文件大小:2379KB
0 235
下载积分: 1 下载次数: 45

代码说明:

  偏最小二乘回归方法及其应用 很有用的书籍 分析数据很有用(Partial least-squares regression method and its application very useful analysis of data useful books)

文件列表:

偏最小二乘回归方法及其应用_0
............................\!00001.pdg
............................\!00002.pdg
............................\!00003.pdg
............................\!00004.pdg
............................\!00005.pdg
............................\!00006.pdg
............................\!00007.pdg
............................\!00008.pdg
............................\!00009.pdg
............................\!00010.pdg
............................\!00011.pdg
............................\!00012.pdg
............................\!00013.pdg
............................\000001.pdg
............................\000002.pdg
............................\000003.pdg
............................\000004.pdg
............................\000005.pdg
............................\000006.pdg
............................\000007.pdg
............................\000008.pdg
............................\000009.pdg
............................\000010.pdg
............................\000011.pdg
............................\000012.pdg
............................\000013.pdg
............................\000014.pdg
............................\000015.pdg
............................\000016.pdg
............................\000017.pdg
............................\000018.pdg
............................\000019.pdg
............................\000020.pdg
............................\000021.pdg
............................\000022.pdg
............................\000023.pdg
............................\000024.pdg
............................\000025.pdg
............................\000026.pdg
............................\000027.pdg
............................\000028.pdg
............................\000029.pdg
............................\000030.pdg
............................\000031.pdg
............................\000032.pdg
............................\000033.pdg
............................\000034.pdg
............................\000035.pdg
............................\000036.pdg
............................\000037.pdg
............................\000038.pdg
............................\000039.pdg
............................\000040.pdg
............................\000041.pdg
............................\000042.pdg
............................\000043.pdg
............................\000044.pdg
............................\000045.pdg
............................\000046.pdg
............................\000047.pdg
............................\000048.pdg
............................\000049.pdg
............................\000050.pdg
............................\000051.pdg
............................\000052.pdg
............................\000053.pdg
............................\000054.pdg
............................\000055.pdg
............................\000056.pdg
............................\000057.pdg
............................\000058.pdg
............................\000059.pdg
............................\000060.pdg
............................\000061.pdg
............................\000062.pdg
............................\000063.pdg
............................\000064.pdg
............................\000065.pdg
............................\000066.pdg
............................\000067.pdg
............................\000068.pdg
............................\000069.pdg
............................\000070.pdg
............................\000071.pdg
............................\000072.pdg
............................\000073.pdg
............................\000074.pdg
............................\000075.pdg
............................\000076.pdg
............................\000077.pdg
............................\000078.pdg
............................\000079.pdg
............................\000080.pdg
............................\000081.pdg
............................\000082.pdg
............................\000083.pdg
............................\000084.pdg
............................\000085.pdg
............................\000086.pdg

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

  • FPGAVHDL
    雷达二维恒虚警算法 是合成孔径雷达的一个重要研究方向(CFAR radar two-dimensional synthetic aperture radar algorithm is an important research direction)
    2009-06-12 23:15:12下载
    积分:1
  • BPSK
    BPSK仿真程序,可以直接运行,保证无错,可统计误码率,观察功率谱变化。(BPSK simulation program can be run directly, to ensure error-free, statistical error rate can be observed power spectrum changes.)
    2011-06-09 03:09:29下载
    积分:1
  • HW1
    Machine Learning: using maximum likelihood, baysiean to learn a graph
    2013-07-17 20:35:40下载
    积分:1
  • Gaussian
    Gaussian filter implementation
    2011-11-08 07:36:43下载
    积分:1
  • 6
    说明:  对标准卡尔曼滤波方法中野值对其滤波精度的影响,提出了基于M估计的野值 处理方法。这种方法不仅可以去除单个野值的影响,而且在野值成片出现时还能很好地保持滤波 稳定性。通过仿真验证了该方法的有效性。(Their filtering accuracy on the standard Kalman filtering method outliers, outliers based on M-estimation approach. This method can not only remove the impact of individual outliers, and opposition values ​ ​ into the film appears to keep the filter can be very stability. The effectiveness of the method is verified by simulation.)
    2013-03-29 11:32:56下载
    积分:1
  • STBC_MATLAB_SOURCE_CODE
    matlab for lpi radar
    2013-11-28 13:07:55下载
    积分:1
  • tegcweev
    可以得到很精确的幅值、频率、相位估计,相控阵天线的方向图(切比雪夫加权),用于信号特征提取、信号消噪,数据模型归一化,模态振动,包括最小二乘法、SVM、神经网络、1_k近邻法,IDW距离反比加权方法。( You can get a very accurate amplitude, frequency, phase estimation, Phased array antenna pattern (Chebyshev weights), For feature extraction, signal de-noising, Normalized data model, modal vibration, Including the least squares method, the SVM, neural networks, 1 _k neighbor method, IDW inverse distance weighting method.)
    2016-02-29 19:43:01下载
    积分:1
  • 用BP神经网络解决分类问题的MATLAB实现
    BP神经网络进行数据分类,是一种多层前馈神经网络,该网络的主要特点是信号前向传递,误差反向传播。运用梯度下降法,(BP neural network is a multi-layer feedforward neural network for data classification. Its main characteristics are forward signal transmission and back error propagation. Using gradient descent method and BP neural network to classify data is a multi-layer feedforward neural network. The main characteristics of this network are forward signal transmission and back error propagation. Using gradient descent method,)
    2019-04-08 11:01:56下载
    积分:1
  • OFDMsubspace
    一个对OFDM信道进行盲估计的子空间算法,具有较好的MSE性能(1 pairs of OFDM blind channel estimation for sub-space algorithm, with better MSE performance)
    2008-03-21 14:08:40下载
    积分:1
  • MATLAB2008
    说明:  是《MATLAB2008应用程序接口编程技术》实例程序代码,用MATLAB实现(Is &quot MATLAB2008 Application Programming Interface,&quot examples of program code, use the MATLAB implementation)
    2010-04-15 11:45:47下载
    积分:1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载