代码搜索：功率谱
找到约 2,083 项符合「功率谱」的源代码
代码结果 2,083
www.eeworm.com/read/350382/10745632
m 5-11.m
%例程5－11 利用基于AR模型的最大熵法估计功率谱
% e.g.5-11.m for example5-11;
% to test function pyulear;
clf;
clear all;
% Generate the signal plus white noise and show
N=1024;
n=0:1/(N-1):1; %Sampling
www.eeworm.com/read/350382/10745651
m 5-10.m
%例程5－10 利用MTM法估计功率谱
% e.g.5-10.m for example5-10.
% to test function pmtm;
clear all;
% Generate the signal with noise and display
N=1024;
n=0:1/(N-1):1; %Sampling frequency N-1
f1=0.1;
www.eeworm.com/read/444759/7607311
m 5-11.m
%例程5－11 利用基于AR模型的最大熵法估计功率谱
% e.g.5-11.m for example5-11;
% to test function pyulear;
clf;
clear all;
% Generate the signal plus white noise and show
N=1024;
n=0:1/(N-1):1; %Sampling
www.eeworm.com/read/444759/7607317
m 5-10.m
%例程5－10 利用MTM法估计功率谱
% e.g.5-10.m for example5-10.
% to test function pmtm;
clear all;
% Generate the signal with noise and display
N=1024;
n=0:1/(N-1):1; %Sampling frequency N-1
f1=0.1;
www.eeworm.com/read/443020/7639326
m duishuzhengtaipu.m
function y=duishuzhengtaipu(a,b,n)
%由数据量为n的高斯白噪声产生向量为n，功率谱为高斯型的对数正态随机向量
%a表示标准方差,b表示均值
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
z1=gaussian(n);
x=gaussianpu(z1);
y=a
www.eeworm.com/read/443020/7639355
asv duishuzhengtaipu.asv
function y=duishuzhengtaipu(a,b,n)
%由数据量为n的高斯白噪声产生向量为n，功率谱为高斯型的对数正态随机向量
%a表示标准方差,b表示均值
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
z1=gaussian(n);
x=gaussianpu(z1);
y=a
www.eeworm.com/read/439949/7696838
m untitled.m
a=0.4;
p=0.2;
b=1.29;
w0=0.8;
T=2*pi/w0;
[t,y]=ode45('liangfun',[0:T/300:400*T],[0.1,0],[],a,b,p,w0);
%画功率谱图
%****************************************************
Y=fft(y(:,1));
Y(1)=[];
n=l
www.eeworm.com/read/299682/7839434
m duishuzhengtaipu.m
function y=duishuzhengtaipu(a,b,n)
%由数据量为n的高斯白噪声产生向量为n，功率谱为高斯型的对数正态随机向量
%a表示标准方差,b表示均值
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
z1=gaussian(n);
x=gaussianpu(z1);
y=a
www.eeworm.com/read/299682/7839494
asv duishuzhengtaipu.asv
function y=duishuzhengtaipu(a,b,n)
%由数据量为n的高斯白噪声产生向量为n，功率谱为高斯型的对数正态随机向量
%a表示标准方差,b表示均值
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
z1=gaussian(n);
x=gaussianpu(z1);
y=a
www.eeworm.com/read/486202/6538111
m 5-11.m
%例程5－11 利用基于AR模型的最大熵法估计功率谱
% e.g.5-11.m for example5-11;
% to test function pyulear;
clf;
clear all;
% Generate the signal plus white noise and show
N=1024;
n=0:1/(N-1):1; %Sampling