代码搜索：Variance
找到约 2,271 项符合「Variance」的源代码
代码结果 2,271
www.eeworm.com/read/297942/7984866
m fig11_28.m
clear all
npts = 2000;
del = 1/2000;
t = 0:del:1;
inp = (1+.2 .* t + .1 .*t.^2) + cos(2. * pi * 2.5 .* t);
X0 = [1,.1,.01]';
% it is assumed that the measurmeny vector H=[1,0,0]
% this is the u
www.eeworm.com/read/297846/7992619
m fig9_28.m
clear all
npts = 2000;
del = 1/2000;
t = 0:del:1;
inp = (1+.2 .* t + .1 .*t.^2) + cos(2. * pi * 2.5 .* t);
X0 = [1,.1,.01]';
% it is assumed that the measurement vector H=[1,0,0]
% this is the
www.eeworm.com/read/297846/7992721
m fig9_27.m
clear all
npts = 2000;
del = 1/2000;
t = 0:del:1;
inp = (1+.2 .* t + .1 .*t.^2);% + cos(2. * pi * 2.5 .* t);
X0 = [1,.1,.01]';
% it is assumed that the measurmeny vector H=[1,0,0]
% this is the
www.eeworm.com/read/398034/8009064
m sa_ex7_8.m
% Maximum Entropy AOA estimation for a M = 6 element array with noise variance = .1
%
M=6;
sig2=.1;
th1=-5*pi/180;
th2=5*pi/180;
a1=[1];
a2=[1];
a=[1];
%u3=[0 0 1 0 0 0];
for i=2:M
a
www.eeworm.com/read/397102/8068032
m scalem.m
%SCALEM Compute scaling map
%
% W = scalem(A)
%
% W is a map that shifts the origin to the mean of the dataset A.
%
% W = scalem(A,'variance')
%
% The origin is shifted to the mean of A and the
www.eeworm.com/read/397102/8068514
m klm.m
%KLM Karhunen-Loeve Mapping (PCA of mean covariance matrix)
%
% [W,alf] = klm(A,n)
% [W,n] = klm(A,alf)
%
% The Karhunen-Loeve Mapping performs a principal component analysis
% (PCA) on the mean cl
www.eeworm.com/read/196380/8095254
m ekf.m
clear;
clc;
K=1;
M=2; %sensor numbers
N=500; % Number of time steps. `
Q=1/3000; % Process noise variance.
R=0.005; % Measurement noise var
www.eeworm.com/read/246805/12703812
m fig11_27.m
clear all
npts = 2000;
del = 1/2000;
t = 0:del:1;
inp = (1+.2 .* t + .1 .*t.^2);% + cos(2. * pi * 2.5 .* t);
X0 = [1,.1,.01]';
% it is assumed that the measurmeny vector H=[1,0,0]
% this is the
www.eeworm.com/read/246805/12703824
m fig11_28.m
clear all
npts = 2000;
del = 1/2000;
t = 0:del:1;
inp = (1+.2 .* t + .1 .*t.^2) + cos(2. * pi * 2.5 .* t);
X0 = [1,.1,.01]';
% it is assumed that the measurmeny vector H=[1,0,0]
% this is the u
www.eeworm.com/read/244800/12843050
m prewhiten.m
function X = prewhiten(X, alfa)
%PREWHITEN Performs prewhitening of a dataset X
%
% X = prewhiten(X)
% X = prewhiten(X, alfa)
%
% Performs prewhitening of the dataset X. Prewhitening concentrates