该网络用于模拟一个sin函数，具体实现如下：
%我思故我在
clc;
clear;
P=-1:0.1:1; %初始值的原始值，该值要经过一系列的岁渐变换用以显示ANN的特性
P2=-1:0.1:1; %用以衡量大小
% T=[0.8 0.66 0.461 0.1336 ...
% -0.1 -0.201 -0.434 -0.5 -0.393 -0.1647 0.0988 0.3072 ...
% 0.396 0.449 0.5816 0.6312 0.7183 0.9201...
% 0.86 0.77 0.65];
T=[ 0.5403 0.6216 0.6967 0.7648 0.8253 0.8776 0.9211 0.9553 0.9801 0.9950 1.0000...
0.9950 0.9801 0.9553 0.9211 0.8776 0.8253 0.7648 0.6967 0.6216 0.5403]
%T为终止值,正好是对应点的sin值
%在途中画出终止值的各个点，r+表示用红色显示
plot(P,T,'r+');
%对初始值P进行随机变换
%%%%%P随机变换开始
[R,Q]=size(P);
[S2,Q]=size(T);
S1=5;
[W1,B1]=rands(S1,R);
[W2,B2]=rands(S2,S1);
b1=[];b2=[];
b1=B1*ones(1,21);
b2=B2*ones(1,21);
a2=W2*tansig(W1*P2+b1)+b2; %Hyperbolic tangent sigmoid transfer function
A2=purelin(a2); %Linear transfer function
%%%%%P随机变换结束
hold on
%显示随机变换后的P的值
plot(P,A2,'g')
hold off
% disp('按任一键继续')
% pause
net=newcf(minmax(P),[5,1],{'tansig','purelin'},'traingd'); %创建两层前向回馈网络
% newcf(PR,[S1 S2...SNl],{TF1 TF2...TFNl},BTF,BLF,PF) takes,
% PR -- R x 2 matrix of min and max values for R input elements
% Si -- Size of ith layer, for Nl layers
% TFi -- Transfer function of ith layer, default = 'tansig'
% BTF -- Backpropagation network training function, default = 'traingd'
% BLF -- Backpropagation weight/bias learning function, default = 'learngdm'
% PF -- Performance function, default = 'mse
net.trainParam.epochs=7000; %初始化训练次数
net.trainParam.goal=9.5238e-005; % sse=0.02 %初始化误差值
%当e的指数为1、2、3时速度较快，但是误差较大，1最大，4比较合适，5以后速度很慢，几乎无法忍受...
%但5以后准确度比较高
net.trainParam.lr = 0.15;
%lr为学习速率，太大不稳定，太小则运算速度慢
%lr为0.2事用时：Elapsed time is 112.687000 seconds.
%lr为0.15时用时：Elapsed time is 117.610000 seconds.
%计时开始
tic;
[net,tr]=train(net,P,T); %训练网络
% net -- New network
% TR -- Training record (epoch and perf)
Y=sim(net,P) ; %计算结果
plot(P,Y,'r-')
hold
plot(P,T,'r+');
hold off
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