登录
首页 » matlab » EnsembleKalman_filter

EnsembleKalman_filter

于 2008-12-02 发布 文件大小:5024KB
0 206
下载积分: 1 下载次数: 6

代码说明:

说明:  集合卡尔曼滤波(EnKF) 数据同化方法可以避免了EKF 中协方差演变方程预报过程中出现的计算不准确和关于协方差矩阵的大量数据的存储问题,最主要的是可以有效的控制估计误差方差的增长,改善预报的效果。(Ensemble Kalman Filter (EnKF) data assimilation methods can be avoided in the EKF covariance forecasting the evolution equation arising in the course of the calculation is not accurate and on the covariance matrix of a large amount of data storage problems, the most important and effective control can be estimated error variance of the growth, improvement in forecasting results.)

文件列表:

EnsembleKalman_filter
.....................\assimilate.m
.....................\bin
.....................\...\L3_step_c.mexa64
.....................\...\L3_step_c.mexglx
.....................\...\L40_step_c.mexa64
.....................\...\L40_step_c.mexglx
.....................\...\QG_step_f.mexa64
.....................\...\QG_step_f.mexglx
.....................\calc_bestrmse.m
.....................\calc_h.m
.....................\calc_k.m
.....................\calc_loccoeffs.m
.....................\calc_r.m
.....................\calc_rlocal.m
.....................\calc_stats.m
.....................\htm" target=_blank>CHANGELOG
.....................\fmain.m
.....................\generate.m
.....................\genU.m
.....................\get_obs.m
.....................\get_pos.m
.....................\get_prm.m
.....................\get_prmstruct.m
.....................\htm" target=_blank>LICENSE
.....................\main.m
.....................\models
.....................\......\common
.....................\......\......\dp5step.m
.....................\......\......\LA_generate_sample.m
.....................\......\......\plot_x.m
.....................\......\......\rk4step.m
.....................\......\......\shuffle.m
.....................\......\L3
.....................\......\..\L3.m
.....................\......\..\L3_step.m
.....................\......\..\L3_step_c.c
.....................\......\..\model_generate.m
.....................\......\..\model_getprmstruct.m
.....................\......\..\model_plotstate.m
.....................\......\..\model_setprm.m
.....................\......\..\model_step.m
.....................\......\L40
.....................\......\...\L40.m
.....................\......\...\L40_step.m
.....................\......\...\L40_step_c.c
.....................\......\...\model_generate.m
.....................\......\...\model_getprmstruct.m
.....................\......\...\model_plotstate.m
.....................\......\...\model_setprm.m
.....................\......\...\model_step.m
.....................\......\L40p
.....................\......\....\generate_samples.m
.....................\......\....\L40p.m
.....................\......\....\L40p_step.m
.....................\......\....\model_generate.m
.....................\......\....\model_getprmstruct.m
.....................\......\....\model_plotstate.m
.....................\......\....\model_setprm.m
.....................\......\....\model_step.m
.....................\......\LA
.....................\......\..\LA_step.m
.....................\......\..\model_generate.m
.....................\......\..\model_getprmstruct.m
.....................\......\..\model_plotstate.m
.....................\......\..\model_setprm.m
.....................\......\..\model_step.m
.....................\......\..\htm" target=_blank>README
.....................\......\LA2
.....................\......\...\LA2_generate_sample.m
.....................\......\...\LA2_step.m
.....................\......\...\model_generate.m
.....................\......\...\model_getprmstruct.m
.....................\......\...\model_plotstate.m
.....................\......\...\model_setprm.m
.....................\......\...\model_step.m
.....................\......\QG
.....................\......\..\f90
.....................\......\..\...\calc.f90
.....................\......\..\...\data.f90
.....................\......\..\...\helmholtz.f90
.....................\......\..\...\Makefile
.....................\......\..\...\mexcmd.m
.....................\......\..\...\mexf90.f90
.....................\......\..\...\nfw.f90
.....................\......\..\...\parameters.f90
.....................\......\..\...\prm-ens.txt
.....................\......\..\...\prm.txt
.....................\......\..\...\qg.f90
.....................\......\..\...\qgflux.f90
.....................\......\..\...\qgplay.m
.....................\......\..\...\qgplot.m
.....................\......\..\...\qgread.m
.....................\......\..\...\qgstep.f90
.....................\......\..\...\qgstep_mex.f90
.....................\......\..\...\htm" target=_blank>README
.....................\......\..\...\utils.f90
.....................\......\..\model_generate.m
.....................\......\..\model_getprmstruct.m
.....................\......\..\model_plotstate.m

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

  • bearing
    excel编制的轴承计算程序,已经在生产设计中应用过(procedures for the preparation of the bearing calculation excel)
    2021-03-28 13:39:11下载
    积分:1
  • fft
    用C++编写的一维和二维快速傅里叶变换程序(Prepared with C++ one-dimensional and two-dimensional fast Fourier transform procedure)
    2013-11-04 16:19:50下载
    积分:1
  • BPalgorithm
    belief propagation algrithm
    2009-10-19 10:58:57下载
    积分:1
  • spgl1-1.8
    based classification (SRC) has been widely used for face recognition (FR). SRC first codes a testing sample as a sparse linear combination of all the training samples, and then classifies the testing sample by evaluating which class leads to the minimum representation error. While the importance of sparsity is much emphasized in SRC and many related works, the use of collaborative representation (CR) in SRC is ignored by most literature.
    2013-08-02 16:14:29下载
    积分:1
  • 二维网格划分
    说明:  划分naca0012网格,其中interfunction为翼型函数(compute mesh for naca0012 airfoil in matlab)
    2020-06-11 08:51:04下载
    积分:1
  • Find-root-Maple
    Newton Raphsson Method For solving Numberical Calculation! Written Ba alireza mahdavi(iran)
    2012-07-05 15:49:22下载
    积分:1
  • MESH-FREE-MLS-SHAPE-FUNCTION
    无网格法移动最小二乘近似(MLS)型函数计算子程序 (Meshless method moving least square approximation (MLS) type function calculation subroutine)
    2013-09-27 10:32:13下载
    积分:1
  • 蚁群法优化参
    说明:  蚁群算法优化随机共振参数,用于滚动轴承故障诊断(Ant colony optimization stochastic resonance parameters, bearing fault diagnosis)
    2021-04-06 13:29:02下载
    积分:1
  • SVD
    用matlab仿真,通过不同的方法计算矩阵的SVD分解,并用SVD的方法计算最小二乘问题和进行图像压缩(Matlab simulation, different ways to calculate the matrix SVD decomposition and the SVD method to calculate the least squares problem and the image compression)
    2012-08-16 12:53:55下载
    积分:1
  • Kriging-ppt
    说明:  克里金方法(Kriging), 是以南非矿业工程师D.G.Krige (克里金)名字命名的一项实用空间估计技术,是地质统计学 的重要组成部分,也是地质统计学的核心。 (Kriging method (Kriging), is a South African mining engineer DGKrige (Kerry King), named after a practical space estimation techniques, is an important part of geostatistics is the core of geostatistics.)
    2020-12-17 15:39:11下载
    积分:1
  • 696518资源总数
  • 106161会员总数
  • 5今日下载