代码搜索：功率电路
找到约 10,000 项符合「功率电路」的源代码
代码结果 10,000
www.eeworm.com/read/274326/10876259
m armodel_psd.m
clear all;
close all;
load test;
N=2048;
% 使用自相关法得到功率谱估计；
[xpsd,F]=pyulear(x,8,N,1);
pmax=max(xpsd);
xpsd=xpsd/pmax;
xpsd=10*log10(xpsd+0.000001);
figure('color','w');subplot(321);
plot(F,
www.eeworm.com/read/469046/6984257
m 14-2.m
I = checkerboard(8);
% 创建一个棋盘图像
PSF = fspecial('gaussian',7,10);
%创建点扩散函数PSF
V = .01;
BlurredNoisy = imnoise(imfilter(I,PSF),'gaussian',0,V);
%对模糊图像添加高斯噪声
NOISEPOWER = V*prod(size(I));
%噪声功率
www.eeworm.com/read/458827/7288811
m rlsxxx.m
%本例比较了在四种特征值扩散度不同的情况下RLS算法的学习曲线
clear all
close all
sigma = 0.001; %噪声功率
NR = 11; %抽头数
N = 300; %采样次数
wn=[3.5 3.3 3.1 2.9];
www.eeworm.com/read/448660/7527510
m nornoise.m
function [Noise] = NorNoise(P, Len)
% 产生0均值高斯噪声行矢量；
% P为噪声交流功率（方差），Len为长度；
% 返回噪声样值
stdNoise = randn(1, Len);
Pfact = sqrt(P./mean(stdNoise.^2));
Noise = stdNoise * Pfact;
% mean(Noise.^2)
www.eeworm.com/read/327991/7532494
m program_18_04_special.m
%采样频率
fs=10000;
nfft=10240;
%轴承外环故障信号
fid=fopen('bearingout1.dat','r');%故障
N=2048;
xdata=fread(fid,N,'int16');
fclose(fid);
xdata=(xdata-mean(xdata))/std(xdata,1);
%功率谱
figure(1);
Y=abs(fft
www.eeworm.com/read/327991/7532497
m program_18_05_special.m
%采样频率
fs=10000;
nfft=10240;
%轴承滚动体故障信号
fid=fopen('bearingroll2.dat','r');%故障
N=2048;
xdata=fread(fid,N,'int16');
fclose(fid);
xdata=(xdata-mean(xdata))/std(xdata,1);
%功率谱
figure(1);
Y=abs(f
www.eeworm.com/read/447711/7546360
m program_18_04_special.m
%采样频率
fs=10000;
nfft=10240;
%轴承外环故障信号
fid=fopen('bearingout1.dat','r');%故障
N=2048;
xdata=fread(fid,N,'int16');
fclose(fid);
xdata=(xdata-mean(xdata))/std(xdata,1);
%功率谱
figure(1);
Y=abs(fft
www.eeworm.com/read/447711/7546362
m program_18_05_special.m
%采样频率
fs=10000;
nfft=10240;
%轴承滚动体故障信号
fid=fopen('bearingroll2.dat','r');%故障
N=2048;
xdata=fread(fid,N,'int16');
fclose(fid);
xdata=(xdata-mean(xdata))/std(xdata,1);
%功率谱
figure(1);
Y=abs(f
www.eeworm.com/read/444759/7607321
m 5-7.m
%例程5－7 功率谱估计的周期图法
% e.g.5-7.m for example5-7;
% to test function of periodogram;
%Estimate the PSD with periodogram method(By formula directly)
clear;
% Case 1: N=256
N=256;
n=0:N-1;
f1=
www.eeworm.com/read/442577/7649282
m get_channel_white_noise.m
function [noise_vector,channel,a]=get_channel_white_noise(channel,signal_sequence)
%获得信道加性高斯白噪声，并且得到的a值用来进行功率分配
if (size(signal_sequence,2)~=0)
S=mean(abs(signal_sequence).^2);
else
S=0;