clear;
% Data input & target
 Data=[...
25.00	2129.19	9.56	0.71	0.61	4.61	7.50	15.25
25.00	2263.45	9.56	1.15	1.04	4.96	7.50	14.84
30.00	1194.69	9.08	0.99	0.89	3.43	7.05	12.82
30.00	1199.59	9.08	0.34	0.49	3.66	7.05	11.28
30.00	1198.08	9.08	1.28	1.15	3.55	7.05	12.47
30.00	1204.72	9.08	0.81	0.70	3.72	7.05	16.04
30.00	1211.29	9.08	0.06	0.09	3.84	7.05	15.20
30.00	1219.33	9.08	1.00	0.90	3.91	7.05	16.10

];

P = Data(:,1:7)';

T = Data(:,8)';
%preprocesing
 [pn,meanp,stdp,tn,meant,stdt]=prestd(P,T)
% Membangun jaringan syaraf feedforward
 net = newff(minmax(P),[9 1],{'logsig'
'purelin' },'trainscg' );
%set bobot
 net.IW{1,1}
net.b{1,1}
net.LW{2,1}
net.b{2,1}
% Melihat bobot-bobot awal input, lapisan dan bias
 Bobotawal_Input = net.IW{1,1};
Bobotawal_Bias_Input = net.b{1,1};
Bobotawal_Lapisan = net.LW{2,1};
Bobotawal_Bias_Lapisan = net.b{2,1};
% Set max epoch, goal, learning rate, momentum, show step
 net.trainParam.epochs =5000;
net.trainParam.goal = 0;
net.trainParam.lr = 0.1;
net.trainParam.show = 100;
net.trainParam.min_grad = 1e-6;
net.trainparam.max_fail = 6;
% Melakukan pembelajaran
 net = train(net,pn,tn);pause
% Melihat bobot-bobot awal input, lapisan dan bias
 Bobotahir_Input = net.IW{1,1};
Bobotahir_Bias_Input = net.b{1,1};
Bobotahir_Lapisan = net.LW{2,1};
Bobotahir_Bias_Lapisan = net.b{2,1};
% Melakukan simulasi
 an=sim(net,pn);
a=poststd(an,meant,stdt);
H=[(1:size(P,2))' T' a' (T'-a')];
sprintf('%2d%9.2f%7.2f%5.2f\n' ,H')
%evaluasi output jaringan(datapelatihan dengan target)
 [m1,a1,r1]=postreg(a,T)