matlab仿真源代码,基于matlab的计算机仿真_源代码.doc
基于matlab的计算机仿真_源代码
例 21
%周期信号(方波)的展开,fb_jinshi.m
close all;
clear all;
N=100; %取展开式的项数为2N＋1项
T=1;
fs=1/T;
N_sample=128; %为了画出波形，设置每个周期的采样点数
dt = T/N_sample;
t=0:dt:10*T-dt;
n=-N:N;
Fn = sinc(n/2).exp(-jn*pi/2);
Fn(N+1)=0;
ft = zeros(1,length(t));
for m=-N:N
ft = ft + Fn(m+N+1)exp(j2pimfst);
end
plot(t,ft)
例 24
利用FFT计算信号的频谱并与信号的真实频谱的抽样比较。
脚本文件T2F.m定义了函数T2F，计算信号的傅立叶变换。
function [f,sf]= T2F(t,st)
%This is a function using the FFT function to calculate a signal's Fourier
%Translation
%Input is the time and the signal vectors,the length of time must greater
%than 2
%Output is the frequency and the signal spectrum
dt = t(2)-t(1);
T=t(end);
df = 1/T;
N = length(st);
f=-N/2df:df:N/2df-df;
sf = fft(st);
sf = T/N*fftshift(sf);
脚本文件F2T.m定义了函数F2T，计算信号的反傅立叶变换。
function [t st]=F2T(f,sf)
%This function computes the time-domain signal by means of Inverse Fast Fourier Transform algorithm using the provided input argument.
%signal's spectrum
df = f(2)-f(1);
Fmx = ( f(end)-f(1) +df);
dt = 1/Fmx;
N = length(sf);
T = dt*N;
%t=-T/2:dt:T/2-dt;
t = 0:dt:T-dt;
sff = fftshift(sf);
st = Fmx*ifft(sff);
另写脚本文件fb_spec.m如下：
%方波的傅氏变换, fb_spec.m
clear all;close all;
T=1;
N_sample = 128;
dt=T/N_sample;
t=0:dt:T-dt;
st=[ones(1,N_sample/2), -ones(1,N_sample/2)]; %方波一个周期
subplot(211);
plot(t,st);
axis([0 1 -2 2]);
xlabel('t'); ylabel('s(t)');
subplot(212);
[f sf]=T2F(t,st); %方波频谱
plot(f,abs(sf)); hold on;
axis([-10 10 0 1]);
xlabel('f');ylabel('|S(f)|');
%根据傅氏变换计算得到的信号频谱相应位置的抽样值
sff= T^2jpif0.5.exp(-j2pifT).sinc(fT0.5).sinc(fT*0.5);
plot(f,abs(sff),'r-')
例 25
%信号的能量计算或功率计算,sig_pow.m
clear all;
close all;
dt = 0.01;
t = 0:dt:5;
s1 = exp(-5t).cos(20pit);
s2 = cos(20pit);
E1 = sum(s1.*s1)*dt; %s1(t)的信号能量
P2 = sum(s2.*s2)*dt/(length(t)*dt); %s2(t)的信号功率s
[f1 s1f]= T2F(t,s1);
[f2 s2f]= T2F(t,s2);
df = f1(2)-f1(1);
E1_f = sum(abs(s1f).^2)*df; %s1(t)的能量,用频域方式计算
df = f2(2)-f2(1);
T = t(end);
P2_f = sum(abs(s2f).^2)*df/T; %s2(t)的功率，用频域方式计算
figure(1)
subpl