PPP = [0 30 33;
    1 13  34;
    2 24  44;
    3 23  55;
    4 34  66;
    5 45  88 ;
    6 56  67;
    7 67  45;
    8 87  32;
    9 49  67;
    10 90 33 ]';

T=[	2458	2337	2256	2228	2275	2381	2515	2648 2818	3016	3146	3140	3070	2970	2894	2913	3154	3391	3380	3295	3193	3041	2825	2581];
[x,y]=size(T);
P = 1:y; 

[pn,meanp,stdp,tn,meant,stdt] = prestd(P,T);

% T = [3 4 6 7 8 7 6 4 3 4 6];

% Here a two-layer feed-forward network is created. The network's input ranges from [0 to 10]. The first layer has five tansig
% neurons, the second layer has one purelin neuron. The trainlm network training function is to be used.

net = newff(minmax(P),[7 1],{'tansig' 'purelin'});

net.trainParam.epochs = 2000;
net = train(net,pn,tn);


% p2 = [1.5 -0.8;0.05 -0.3];
% [p2n] = trastd(p2,meanp,stdp);
% an = sim(net,pn);
% [a] = poststd(an,meant,stdt);

[a,b]=size(P);
X = [1:b];
Y = sim(net,pn);
figure
plot(X,tn,X,Y,'o')