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/128332/6304259
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/309192/6342017
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/309192/6342031
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/309192/6342033
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/309192/6342040
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/492901/6412293
cpp d9r1.cpp
#include "iostream.h"
#include "stdlib.h"
#include "math.h"
void main()
{
//program d9r1
//driver for routine fit
int i,mwt,npt = 100;
double spread = 0.5;
double x[101]
www.eeworm.com/read/488461/6487292
m normal.m
function y=normal(x,m,s)
% FUNCTION y=NORMAL(x,m,s)
% Gaussian distribution
% m=mean
% s=standard deviation
y=(1/sqrt(2*pi*s^2))*exp(-((x-m).^2)/(2*s^2));
www.eeworm.com/read/488464/6487357
m normal.m
function y=normal(x,m,s)
% FUNCTION y=NORMAL(x,m,s)
% Gaussian distribution
% m=mean
% s=standard deviation
y=(1/sqrt(2*pi*s^2))*exp(-((x-m).^2)/(2*s^2));
www.eeworm.com/read/485122/6564953
cpp d9r1.cpp
#include "iostream.h"
#include "stdlib.h"
#include "math.h"
void main()
{
//program d9r1
//driver for routine fit
int i,mwt,npt = 100;
double spread = 0.5;
double x[101]
www.eeworm.com/read/477304/6741464
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 nois