代码搜索：deviation
找到约 1,443 项符合「deviation」的源代码
代码结果 1,443
www.eeworm.com/read/295808/8138776
m main.m
% Program 2-3
% main.m
%
% calculate mean, dispersion and standard deviation for the vector data
%
% Programmed by H.Harada
%
data=rand(1,20);%产生均匀分布的随机数
mvalue2=mvalue(data); %sum(data)/len
www.eeworm.com/read/145776/12703070
m vlms.m
%VLMS Volterra LMS algorithm
%
% 'ifile.mat' - input file containing:
% Nr - members of ensemble
% dim - iterations
% Sx - standard deviation of input
% Sn - standard deviation
www.eeworm.com/read/145776/12703111
m lms3.m
%LMS3 Problem 1.1.1.2.1
%
% 'ifile.mat' - input file containing:
% I - members of ensemble
% K - iterations
% a1 - coefficient of input AR process
% sigmax - standard dev
www.eeworm.com/read/145776/12703127
m rls3.m
%RLS3 Problem 1.1.1.2.3
%
% 'ifile.mat' - input file containing:
% I - members of ensemble
% K - iterations
% a1 - coefficient of input AR process
% sigmax - standard dev
www.eeworm.com/read/145776/12703134
m vrls.m
%VRLS Volterra RLS algorithm
%
% 'ifile.mat' - input file containing:
% Nr - members of ensemble
% dim - iterations
% Sx - standard deviation of input
% Sn - standard deviation
www.eeworm.com/read/145776/12703159
m nlrls2.m
%NLRLS2 Problem 1.1.1.2.5
%
% 'ifile.mat' - input file containing:
% I - members of ensemble
% K - iterations
% a1 - coefficient of input AR process
% sigmax - standard d
www.eeworm.com/read/246803/12704269
m gwn.m
function B=GWN(n,beta)
% GWN- Generation of Gaussian White Noise
% Usage
% B=GWN(n,beta)
% Inputs
% n size of datas
% beta standard deviation
% Outputs
% B resulting noise
www.eeworm.com/read/246803/12704324
m gwn2.m
function B=GWN2(n,beta)
% GWN2- Generation of 2-D Gaussian White NNoise
% Usage
% B=GWN2(n,beta)
% Inputs
% n size of datas
% beta standard deviation
% Outputs
% B resulting
www.eeworm.com/read/332054/12782796
m gngauss.m
function[gsrv1,gsrv2]=gngauss(m,sgma)
% m--mean, sgma--standard deviation
if nargin==0,
m=0;sgma=1;
elseif nargin==1,
sgma=m;m=0;
end;
u=rand;
z=sgma*(sqrt(2*log(1/(1-u)))); %a R
www.eeworm.com/read/332054/12782810
m gngauss.m
function[gsrv1,gsrv2]=gngauss(m,sgma)
% m--mean, sgma--standard deviation
if nargin==0,
m=0;sgma=1;
elseif nargin==1,
sgma=m;m=0;
end;
u=rand;
z=sgma*(sqrt(2*log(1/(1-u)))); %a R