clear;
% Data input & target
 Data=[...
2	2129.19	9.56	0.708	0.608	4.608	7.5	3.76
2	2263.45	9.56	1.15	1.035	4.964	7.5	2.82
1	1194.69	9.08	0.989	0.89	3.425	7.05	1.96
1	1199.59	9.08	0.335	0.491	3.656	7.05	2.03
1	1198.08	9.08	1.275	1.148	3.554	7.05	1.52
1	1204.72	9.08	0.806	0.703	3.724	7.05	3.05
1	1211.29	9.08	0.059	0.092	3.84	7.05	3.29
1	1219.33	9.08	1	0.9	3.914	7.05	6.03

];

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)