代码搜索：M
找到约 10,000 项符合「M」的源代码
代码结果 10,000
www.eeworm.com/read/486900/6519755
m m10_6.m
%通过下面两个参数值的改变比较结果
%定义要逼近的非线性函数
p = [-1:.05:1];
t = sin(pi*p);
plot(p,t,'-')
title('要逼
www.eeworm.com/read/486900/6519756
m m10_3.m
P=[1.0 1.5 1.2 -0.3
-1.0 2.0 3.3 -0.5
2.0 1.0 -1.6 0.9];
T=[0.5 3.0 -2.2 1.4
1.1 -1.2 1.7 -0.4
3.0 0.2 -1.8 -0.4
-1.0 0.1 -1.0 0.6];
[Q,R]=size(P);
www.eeworm.com/read/486900/6519757
m m10_7.m
%要逼近的函数样本点
P = -1:.1:1;
T=sin(pi*P);
plot(P,T,'+');
title('待逼近的函数样本点');
xlabel('输入值
www.eeworm.com/read/486900/6519758
m m10_1.m
%输入样本点及其相应的类别
P = [-0.5 -0.5 0.3 -0.1 0.2 0.0 0.6 0.8;
-0.5 0.5 -0.5 1.0 0.5 -0.9 0.8 -0.6];
T = [1 1 0 1 1 0 1 0 ];
%在坐标图上绘出样本点
plotpv(P,T);
%使用newp函数建立一个
www.eeworm.com/read/486900/6519759
m m10_8.m
%指定存储在网络中的目标平衡点
T = [+1 +1;-1 +1; -1 -1];
axis([-1 1 -1 1 -1 1]);
set(gca,'box','on');
axis manual;
hold
www.eeworm.com/read/486900/6519760
m m10_5.m
close all
clear
echo on
clc
% NEWFF——生成一个新的前向神经网络
% TRAIN——对 BP 神经网络进行训练
www.eeworm.com/read/486900/6519761
m m10_9.m
close all
clf reset
figure (gcf);
clc
% NEWSOM——创建自组织网络
% TRAI
www.eeworm.com/read/486900/6519762
m m10_2.m
clear
figure(gcf)
echo on
clc
%NEWP — 建立一个感知器
%INIT — 初始化感知器神经元
%SIM — 对感知器神经元仿真
%TRAIN — 训练感知器神经元
pause % 键入任意键继续
P = [-1 +1 -1 +1 -1 +1 -1 +1;
-1 -1 +1 +1 -1 -1 +1 +1;
-1 -1 -1
www.eeworm.com/read/486900/6519763
m m10_4.m
%定义输入信号并绘出其曲线
time=0:0.025:5;
X=sin(sin(time).*time*10);
plot(time,X);
title('输入信号X');
xlabel('时间');
www.eeworm.com/read/486900/6519764
m m9_1.m
%H2控制器设计程序：
s=zpk('s');
G=(s-1)/(s+1);
W1=0.1*(s+100)/(100*s+1); W2=0.1; W3=[];
P=augw(G,W1,W2,W3);
[K,CL,GAM]=h2syn(P);
K,GAM
%H无穷最优控制器设计程序：
s=zpk('s');
G=(s-1)/(s+1);
W1=0.1*(s+100)/(1