代码搜索：Estimation
找到约 3,786 项符合「Estimation」的源代码
代码结果 3,786
www.eeworm.com/read/470787/6908304
txt rls 算法.txt
% RLS 算法
http://emuch.net/html/200605/241656.html
http://www.5zu.cn/sitemaps/sitemap_82.html
randn('seed', 0) ;
rand('seed', 0) ;
NoOfData = 8000 ; % Set no of data points used for training
www.eeworm.com/read/469779/6927054
m chap7_10f.m
%Discrete Kalman filter
%x=Ax+B(u+w(k));
%y=Cx+D+v(k)
function [u]=kalman(u1,u2,u3)
persistent A B C D Q R P x
yv=u2;
if u3==0
x=zeros(2,1);
ts=0.001;
a=25;b=133;
sys=tf(b,[1,a
www.eeworm.com/read/212797/6962497
m vtb3.m
% VTB3
%
% VTB3_1 Impulse response of a SDOF system.
% VTB3_2 Step response of a SDOF system.
% VTB3_3 Fourier Series estimation.
% VTB3_4 Response spectrum for a SDOF system.
% VTB3_5 Plot Fourier S
www.eeworm.com/read/468647/6986171
m hmtdeno0.m
function [yw] = hmtdeno0(w,L,ES,PS,MU,SI)
% function [yw] = hmtdeno0(w,L,ES,PS,MU,SI)
%
% Author: H. Choi
% Last modified: 12/22/1998
%
% input :
% w : noisy wavelet coeffs
% (normalize image p
www.eeworm.com/read/468647/6986185
m hmtdeno.m
function [yw] = hmtdeno(w,L,ES,PS,MU,SI)
% function [yw] = hmtdeno(w,L,ES,PS,MU,SI)
%
% Author: H. Choi
% Last modified: 12/14/1998
%
% input :
% w : noisy wavelet coeffs
% (normalize image pix
www.eeworm.com/read/467598/7005789
m chap7_10f.m
%Discrete Kalman filter
%x=Ax+B(u+w(k));
%y=Cx+D+v(k)
function [u]=kalman(u1,u2,u3)
persistent A B C D Q R P x
yv=u2;
if u3==0
x=zeros(2,1);
ts=0.001;
a=25;b=133;
sys=tf(b,[1,a
www.eeworm.com/read/299984/7140302
m parzenml3.m
%PARZENML Optimum smoothing parameter in Parzen density estimation.
%
% H = PARZENML(A,FID)
%
% INPUT
% A input dataset
% FID File ID to write progress to (default [], see PRPROGRESS)
%
%
www.eeworm.com/read/333209/7154860
m kf_cwpa_demo.m
% Demonstration for Kalman filter and smoother using a 2D CWPA model
%
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or lat
www.eeworm.com/read/460981/7236117
m rls.m
clear;
clc;
randn('seed', 0) ;
rand('seed', 0) ;
NoOfData = 1785 ;
Order =40 ;
Lambda = 0.98;
Delta = 0.001 ;
x =randn(NoOfData,1);
t=0:((4*pi)/1784):4*pi;
x=x+(sin(t))';
h
www.eeworm.com/read/460978/7236133
m lms1.m
randn('seed', 0) ;
rand('seed', 0) ;
NoOfData = 1785 ; % Set no of data points used for training
Order = 40 ; % Set the adaptive filter order
Mu = 0.01 ; % Set the step-size constant
x = ra