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/38039/1095492
mnu cav_stop.mnu
STOP#CAV
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Show#Results
Display results of completed deviation calculations.
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Resume#Calcs
Resume Calculations.
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Abort#Calcs
Abort Calculations.
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www.eeworm.com/read/38039/1099368
mnu cav_stop.mnu
STOP#CAV 氨ゎ璸衡熬畉
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Show#Results 陪ボ挡狦
Display results of completed deviation calculations.
陪ボЧΘ
www.eeworm.com/read/461848/1549658
m stats_3.m
% Script file: stats_3.m
%
% Purpose:
% To calculate mean and the standard deviation of
% an input data set, where each input value can be
% positive, negative, or zero.
%
% Reco
www.eeworm.com/read/204986/5026573
java bestguesscompoundtracker.java
package org.placelab.client.tracker;
import java.util.Enumeration;
/**
* A CompoundTracker that simply returns whichever estimate
* has the lowest standard deviation.
*/
public class BestGuessCo
www.eeworm.com/read/279486/4136379
m cellstd.m
function st = cellstd(x)
%CELLSTD Calculate standard deviation of each cell
% X = CELLSTD(X)
% Scott J Gaffney 11 September 2001
% Department of Information and Computer Science
% Universi
www.eeworm.com/read/476296/6765140
m dlcllms.m
%DLCLLMS Delayless Closed-Loop Subband LMS algorithm
%
% 'ifile.mat' - input file containing:
% Nr - members of ensemble
% N - iterations
% Sx - standard deviation of input
% B
www.eeworm.com/read/476296/6765147
m sfrls3.m
%SFRLS3 Problem 2.3
%
% 'ifile.mat' - input file containing:
% K - iterations
% H - FIR channel
% Neq - equalizer order
% sigman - standard deviation of measurement noise
www.eeworm.com/read/476296/6765172
m eflrls3.m
%EFLRLS3 Problem 2.2
%
% 'ifile.mat' - input file containing:
% K - iterations
% H - FIR channel
% Neq - equalizer order
% sigman - standard deviation of noise at channel
www.eeworm.com/read/476296/6765180
m olsblms.m
%OLSBLMS Open-Loop Subband LMS algorithm
%
% 'ifile.mat' - input file containing:
% Nr - members of ensemble
% N - iterations
% Sx - standard deviation of input
% B - coefficien
www.eeworm.com/read/293183/8310777
m gencirc.m
%GENCIRC Generation of a one-class circular dataset
%
% A = gencirc(n,s)
%
% Generation of a one-class circular dataset with radius 1 and
% normally distributes radial noise with standard deviation