wf2.m

《MATLAB工具箱的神经网络理论与应用》丛爽编

% wf2.m
%
P = [ 1 1.5 1.2 -0.3 ; -1 2 3 -0.5 ;2 1 -1.6 0.9 ] ;
T = [0.5 3 -2.2 1.4 ; 1.1 -1.2 1.7 -0.4 ;3 0.2 -1.8 -0.4; -1 0.1 -1.0 0.6];
[S,Q] = size(T);
lr = 0.9*maxlinlr (P);
W0 = [ 1.9978 -0.5959 -0.3517; 1.5543 0.05331 1.3660; 		% 初始权值
     1.0672 0.3645 -0.9227; -0.7747 1.3839 -0.3384];
B0 = [ 0.0746;-0.0642;-0.4256;-0.6433];
net = newlin(minmax(P),S,[0],lr);                           % 创建线性网络
net.iw{1,1} = W0;									
net.b{1} = B0;
A = sim(net,P);                                 
e = T - A;                                                  % 求训练前网络的输出误差
sse = (sumsqr(e))/(S*Q);                                        % 求误差平方和的平均值
fprintf('Before training,sum squrared error=%g.\n',sse);    % 显示训练前网络的均方差
net.trainParam.epochs = 400; 			                    % 最大循环次数
net.trainParam.goal = 0.001; 			                    % 期望误差(均方差)
[net,tr]=train(net,P,T);                                
W = net.iw{1,1}                                             % 显示最终权值
B = net.b{1}