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- 随机序列发生器,是一个m序列,生成函数都写在里面,位宽为4,可以改变!-random sequence generator, m is a sequence, generating function will be included in the inside, for four bit-can be changed!
Update : 2008-10-13 Size : 115.03kb Publisher : lw234620 - Box-Muller变换，将[0,1]均匀分布转换为[0,1]高斯正态分布，在matlab中也可用randn函数生成正态分布。变换的思想可用于其他没有正态分布随机函数的编程语言（如C）
Update : 2008-10-13 Size : 540byte Publisher : 方俊彬 - 随机序列发生器,是一个m序列,生成函数都写在里面,位宽为4,可以改变!-random sequence generator, m is a sequence, generating function will be included in the inside, for four bit-can be changed!
Update : 2025-04-07 Size : 115kb Publisher : lw234620 - Box-Muller变换，将[0,1]均匀分布转换为[0,1]高斯正态分布，在matlab中也可用randn函数生成正态分布。变换的思想可用于其他没有正态分布随机函数的编程语言（如C）
Update : 2025-04-07 Size : 1kb Publisher : 方 - Visual C++写的，用于生成服从高斯分布的伪随机数的代码，没有位宽为4的限制。-to generate Gaussian random numbers
Update : 2025-04-07 Size : 1kb Publisher : LinJL - The file calculates and plots FM noise sidebands for a carrier. It also does sinusoidal modulation. This simple way of adding noise to a carrier is useful for simulation of PLLs. It turns out, though, that the mean of the randn function is not as close to zero as it could be, and this causes the fft to generate extra sideband energy, which makes it appear as if the noise is not what would be expected. This program gets around this by adding a fudge factor to the randn results to eliminate this problem. It took me ages to figure this out, I hope to reduce a similar effort for others.
Update : 2025-04-07 Size : 2kb Publisher : Rafal - IS95中的数据流伪随机序列的产生。用matlab中randn实现-IS95 data streams to be pseudo-random sequence generation. Achieved using randn in matlab
Update : 2025-04-07 Size : 8kb Publisher : lixiao - 瑞丽信道仿真
噪声信号由MATLAB函数randn(1,N)产生，它从均值为0、方差为1的正态分布中产生N个伪随机数。每次迭代时，要使用相应的标准差对噪声的幅度进行尺度变换，最后，将输入信号和噪声信号相加得到输出信号。-Ruili channel simulation noise signal by the MATLAB function randn (1, N) generated, which from the mean 0, variance for a normal distribution N generate a pseudo-random numbers. Each iteration, it is necessary to use the corresponding standard deviation of the noise amplitude scaling, finally, the input signal and the noise signal to be added to the output signal.
Update : 2025-04-07 Size : 1kb Publisher : eunice - rapresents AR(1) model. dsp and correlation funtion of the ar(1) MODEL. it uses randn to generate vector and filter to do ar(1).
Update : 2025-04-07 Size : 1kb Publisher : lelepal - w=(randn(1,M)-randn(1,M))/100
d=zeros(1,M)
u=zeros(1,M)
u_out=zeros(1,e_max-M)
f_out=zeros(1,e_max-M)
X delayed input data vector
Y measured signal
W coefficient vector
E enhanced signal
clc
N=30 filter length
M=30 delay
w0=1 initial value for adaptive filter coefficients
SF=2048 factor for reducing the data samples - 11 bit ADC assumed
mu=0.04 - w=(randn(1,M)-randn(1,M))/100
d=zeros(1,M)
u=zeros(1,M)
u_out=zeros(1,e_max-M)
f_out=zeros(1,e_max-M)
X delayed input data vector
Y measured signal
W coefficient vector
E enhanced signal
clc
N=30 filter length
M=30 delay
w0=1 initial value for adaptive filter coefficients
SF=2048 factor for reducing the data samples - 11 bit ADC assumed
mu=0.04
Update : 2025-04-07 Size : 1kb Publisher : venkat - 常用的产生通用特殊矩阵的函数有： zeros：产生全0矩阵。 ones：产生全1矩阵。 eye：产生单位矩阵。 rand：产生0～1间均匀分布的随机矩阵。 randn：产生均值为0，方差为1的标准正态分布随机-Production of commonly used generic special matrix functions are: zeros: produce the 0 matrix. ones: generate all 1 matrix. eye: production unit matrix. rand: produce 0 ~ a uniformly distributed random matrix. randn: generate mean 0, variance 1, the standard normal random
Update : 2025-04-07 Size : 185kb Publisher : wgy - matlab
switch lower(type)
case uniform
R=a+b*randn(M,N)
case uniform
R= a+(b - a)*rand(M,N-
switch lower(type)
case uniform
R=a+b*randn(M,N)
case uniform
R= a+(b- a)*rand(M,N
Update : 2025-04-07 Size : 1kb Publisher : jf - 例4：在sinc(t)信号中叠加噪声，分析对应频谱
fs=100 采样频率,必须大于两倍基带信号最高频率
ts=1/fs 采样时间间隔
T=2 时间窗大小
-
x=x+0.1*randn(1,N)
x=awgn(x,-10, measured )
figure
plot(t,x)
title( 时域信号图 )
xlabel( t /s )
y=fft(x,N)
figure
if mod(N,2)~=0
N=N-1
end
f=linspace(0,fs/2,N/2)
plot(f,abs(y(1:N/2))*2/N)
title( 叠加噪声的sinc(t)信号频谱 )
xlabel( f /Hz )
figure
y0=y(1:N/2)
P=y0.*conj(y0)/N
P=10*log10(P)
plot(f,P)
title( 功率谱 )
xlabel( f /Hz )
Update : 2025-04-07 Size : 1kb Publisher : 张红 - 基于IFFT\FFT的OFDM系统仿真。完整的可运行的代码。信源用randn产生，信道为高斯白噪声，如用其他，可以方便自行修改。-Based on IFFT \ FFT of OFDM system simulation. Complete code can be run. Generated using randn source, channel for the Gaussian white noise, such as the use of other, you can easily modify.
Update : 2025-04-07 Size : 1kb Publisher : 彭俊 - Avetis Ioannisyan
avetis@60ateight.com
Last Updated: 11/30/05
LMS Channel Adaptation
reset randomizers
randn( state ,sum(100*clock))
rand( state ,sum(100*clock))
numPoints = 5000
numTaps = 10 channel order
Mu = 0.001:0.001:0.01 iteration step size
input is guassian
x = randn(numPoints,1) + j*randn(numPoints,1)
choose channel to be random uniform
h = rand(numTaps, 1) + i*rand(numTaps, 1)
h = [1 0 0 0 1] testing only
h = h/max(h) normalize channel
convolve channel with the input
d = filter(h, 1, x)
initialize variables
w = []
y = []
in = []
e = [] error, f- Avetis Ioannisyan
avetis@60ateight.com
Last Updated: 11/30/05
LMS Channel Adaptation
reset randomizers
randn( state ,sum(100*clock))
rand( state ,sum(100*clock))
numPoints = 5000
numTaps = 10 channel order
Mu = 0.001:0.001:0.01 iteration step size
input is guassian
x = randn(numPoints,1) + j*randn(numPoints,1)
choose channel to be random uniform
h = rand(numTaps, 1) + i*rand(numTaps, 1)
h = [1 0 0 0 1] testing only
h = h/max(h) normalize channel
convolve channel with the input
d = filter(h, 1, x)
initialize variables
w = []
y = []
in = []
e = [] error, f
Update : 2025-04-07 Size : 1kb Publisher : josh - clear all
clc
t=0:1/1000:10-1/1000
s=sin(2*pi*t)
snr=20
s_power=var(s) varience of s
linear_snr=10^(snr/10)
factor=sqrt(s_power/linear_snr)
noise=randn(1,length(s))*factor
x=s+noise Ó É SNR¼ Æ Ë ã Ë æ » úÔ ë É ù
x1=noise Ô ë É ùÔ ´ Ê ä È ë
x2=noise
w1=0 È ¨Ï µ Ê ý ³ õ Ö µ
w2=0
e=zeros(1,length(x))
y=0
u=0.05
for i=1:10000 LMSË ã ·¨
y(i)=w1*x1(i)+w2*x2(i)
e(i)=x(i)-y(i)
w1=w1+u*e(i)*x1(i)
w2=w2+u*e(i)*x2(i)
end
figure(1)
subplot(4,1,1) -clear all
clc
t=0:1/1000:10-1/1000
s=sin(2*pi*t)
snr=20
s_power=var(s) varience of s
linear_snr=10^(snr/10)
factor=sqrt(s_power/linear_snr)
noise=randn(1,length(s))*factor
x=s+noise Ó É SNR¼ Æ Ë ã Ë æ » úÔ ë É ù
x1=noise Ô ë É ùÔ ´ Ê ä È ë
x2=noise
w1=0 È ¨Ï µ Ê ý ³ õ Ö µ
w2=0
e=zeros(1,length(x))
y=0
u=0.05
for i=1:10000 LMSË ã ·¨
y(i)=w1*x1(i)+w2*x2(i)
e(i)=x(i)-y(i)
w1=w1+u*e(i)*x1(i)
w2=w2+u*e(i)*x2(i)
end
figure(1)
subplot(4,1,1)
Update : 2025-04-07 Size : 1kb Publisher : dasu - cma的MATLAB仿真程序，已经仿真过，供大家参考-cma of the MATLAB simulation program has been simulated for reference
Update : 2025-04-07 Size : 3kb Publisher : 张福生 - function func_baidu_monte_carlo randn( state , sum(100*clock)) 利用时钟设置随机种子，这样每次产生的随机数就不同了 -function func_baidu_monte_carlo randn( state , sum(100*clock)) 利用时钟设置随机种子，这样每次产生的随机数就不同了
Update : 2025-04-07 Size : 19kb Publisher : 肖 - The file calculates and plots FM noise sidebands for a carrier. It also does sinusoidal modulation. This simple way of adding noise to a carrier is useful for simulation of PLLs. It turns out, though, that the mean of the randn function is not as close to zero as it could be, and this causes the fft to generate extra sideband energy, which makes it appear as if the noise is not what would be expected. This program gets around this by adding a fudge factor to the randn results to eliminate this problem. It took me ages to figure this out, I hope to reduce a similar effort for others
Update : 2025-04-07 Size : 3kb Publisher : zfhou - HISTNORM Histogram normalized
[...] = HISTNORM(...) works like HIST, but the frequency is normalized
so that area sum is 1.
Bonus usage!
[...] = HISTNORM(..., plot ) plots and returns the output arguments.
Be sure plot is the last argument.
Example:
data = randn (10000, 1)
[xo,no] = histnorm(data, 101, plot )
hold on
plot (no, normpdf(no), r )
hold off
See also: HIST.- HISTNORM Histogram normalized
[...] = HISTNORM(...) works like HIST, but the frequency is normalized
so that area sum is 1.
Bonus usage!
[...] = HISTNORM(..., plot ) plots and returns the output arguments.
Be sure plot is the last argument.
Example:
data = randn (10000, 1)
[xo,no] = histnorm(data, 101, plot )
hold on
plot (no, normpdf(no), r )
hold off
See also: HIST.
Update : 2025-04-07 Size : 1kb Publisher : Cystrin