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/460435/7250777
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/450608/7480379
m parzenml.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/449038/7519743
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/448535/7531560
m wrls.m
function [h,eap] = wrls(x,d)
%
% Given a scalar input signal x and a desired scalar signal d,
% compute an RLS update of the weight vector h.
% eap is an optional return parameter, the a-priori e
www.eeworm.com/read/448050/7541031
m demo.m
% Dummy Script for OFDM Channel Estimation
[y, A, x0, group, err] = GenerateProblem(64, 32, 8, 5, 0, 0);
x = l2p_re(A,y,0.4,group);
stem(x0), hold on, stem(x,'r+')
s=SL20(A, y, group, 0.00001);
s
www.eeworm.com/read/447973/7542727
m viewtrajectories.m
%%% DynaEst 3.032 10/22/2000
% Copyright (c) 2000 Yaakov Bar-Shalom
%
%ViewTrajectories
subplot(1,1,1)
set(view_legend_menu,'Enable','on')
set(view_grid_menu,'Enable','on')
ViewStatusFlag
www.eeworm.com/read/444331/7613799
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/442757/7645314
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/441245/7672991
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/439850/7700742
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