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/205695/15309686
m chap6_3.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/299343/3853261
m chap6_3.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/449781/1673966
m chap6_2.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/475897/6768468
m chap6_3.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/238364/13891342
m chap6_3.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/236814/13997929
m chap6_3.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/132947/14065514
m chap6_2.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/132945/14065546
m chap6_2.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/192188/8400188
xml tmpindex.xml
<Emphasis>Learning Perl, Third
Edition<Default Para Font
www.eeworm.com/read/467873/7003103
m perceptron.m
%perceptron.m - perceptron learning algorithm
% INput: train(:,1:M) - pattern train(:,M+1) - target
% Output: weight vector w=[w0 w1 ... wM], w0: bias
% actual output vector y
% Need to call m-fi