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/422591/10627072
rd ks-package.rd
\name{ks}
\alias{ks}
\docType{package}
\title{
ks
}
\description{
Kernel density estimation and kernel discriminant analysis for
data from 1- to 6-dimensions, with display functions.
}
\details
www.eeworm.com/read/422590/10627292
rd cde.rd
\name{cde}
\alias{cde}
\title{Conditional Density Estimation}
\description{Calculates kernel conditional density estimate using local
polynomial estimation.}
\usage{
cde(x, y, deg = 0, link
www.eeworm.com/read/273090/10928048
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/273065/10929478
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/417106/11003731
m hybridbody.m
function [AltErr, VelErr, BallErr] = HybridBody
% Hybrid extended Kalman filter example.
% Track a body falling through the atmosphere.
% Outputs are:
% AltErr = RMS altitude estimation error
www.eeworm.com/read/417106/11003767
m extendedbody.m
function [AltErr, VelErr, BallErr] = ExtendedBody
% Continuous time etended Kalman filter example.
% Track a body falling through the atmosphere.
% Outputs are:
% AltErr = RMS altitude estimat
www.eeworm.com/read/417106/11003770
m kalmanconstrained.m
function KalmanConstrained
% function KalmanConstrained
% This m-file simulates a vehicle tracking problem.
% The vehicle state is estimated with a Kalman filter.
% In addition, with the a prior
www.eeworm.com/read/417106/11003789
m discretekfex1.m
function DiscreteKFEx1(N)
% Discrete time Kalman filter for position estimation of a Newtonian system.
% This example illustrates the effectiveness of the Kalman filter for state
% estimation. It
www.eeworm.com/read/416411/11030714
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/416230/11037483
m program_15_4.m
% Program 15_4
% Power Spectrum Estimation Using Welch's Method
%
n = 0:1000;
g = 2*sin(0.12*pi*n) + sin(0.28*pi*n) + randn(size(n));
nfft = input('Type in the fft size = ');
window = hamming(25