代码搜索：Learning
找到约 5,352 项符合「Learning」的源代码
代码结果 5,352
www.eeworm.com/read/267665/11169670
m single neural net pid decouple controller based on hebb learning.m
%Single Neural Net PID Decouple Controller based on Hebb Learning
%Algorithm to adjust kp,ki,kd
clear all;
close all;
xc1=[0,0,0]';
xc2=[0,0,0]';
xiteP=0.40;
xiteI=0.40;
xiteD=0.40;
%I
www.eeworm.com/read/237257/13970319
chm oreilly.learning.the.bash.shell.3rd.edition.mar.2005.chm
www.eeworm.com/read/191902/8417446
txt preprocessing.txt
ADDC@Number of partitions:@4@S
AGHC@Number of partitions, Distance:@[4, 'min']@S
BIMSEC@Num of partitions, Nattempts:@[4, 1]@S
Competitive_learning@Number of partitions, eta:@[4, .01]@S
Determinis
www.eeworm.com/read/289680/8535066
bib manual.bib
@misc{Bay1999,
author = "Bay, S. D.",
title = "The {UCI} {KDD} Archive
$[$\texttt{http://kdd.ics.uci.edu/}$]$",
howpublished = "University of California, D
www.eeworm.com/read/188280/8552195
bib manual.bib
@misc{Bay1999,
author = "Bay, S. D.",
title = "The {UCI} {KDD} Archive
$[$\texttt{http://kdd.ics.uci.edu/}$]$",
howpublished = "University of California, D
www.eeworm.com/read/428849/8834869
m~ contents.m~
% Algorithms learning linear classifiers from finite vector sets.
%
% ekozinec - Kozinec's algorithm for eps-optimal separating hyperplane.
% ekozinec2 - Kozinec's algorithm for eps-optimal separ
www.eeworm.com/read/183443/9158894
bib manual.bib
@misc{Bay1999,
author = "Bay, S. D.",
title = "The {UCI} {KDD} Archive
$[$\texttt{http://kdd.ics.uci.edu/}$]$",
howpublished = "University of California, D
www.eeworm.com/read/181389/9256496
bib manual.bib
@misc{Bay1999,
author = "Bay, S. D.",
title = "The {UCI} {KDD} Archive
$[$\texttt{http://kdd.ics.uci.edu/}$]$",
howpublished = "University of California, D
www.eeworm.com/read/181388/9256641
bib manual.bib
@misc{Bay1999,
author = "Bay, S. D.",
title = "The {UCI} {KDD} Archive
$[$\texttt{http://kdd.ics.uci.edu/}$]$",
howpublished = "University of California, D
www.eeworm.com/read/177129/9469059
txt preprocessing.txt
ADDC@Number of partitions:@4@S
AGHC@Number of partitions, Distance:@[4, 'min']@S
BIMSEC@Num of partitions, Nattempts:@[4, 1]@S
Competitive_learning@Number of partitions, eta:@[4, .01]@S
Determinis