% 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; 		% 初始權值
     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);                           % 創建線性網絡
net.iw{1,1} = W0;									
net.b{1} = B0;
A0 = sim(net,P),         
e = T - A0;                                                  % 求訓練前網絡的輸出誤差
sse = (sumsqr(e))/(S*Q);                                        % 求誤差平方和的平均值
fprintf('Before training,sum squrared error=%g.\n',sse);    % 顯示訓練前網絡的均方差
net.trainParam.epochs = 400; 			                    % 最大循環次數
net.trainParam.goal = 0.001; 			                    % 期望誤差(均方差)
[net,tr]=train(net,P,T);                                
W = net.iw{1,1}                                             % 顯示最終權值
B = net.b{1}
A = sim(net,P),         
A1=purelin(W*P,B)