How can I build a 1x5x1 NN?

Hi All, I am trying to build a FF NN with delay between layers to reproduce an abstract. My aim is to build the following 3 layers NN 1x5x1 with 5 delays taps between layers (5:5). So far this is the code I used to programmed but I'm not sure my code is building the network I want. Can anybody help me to check if my design is correct? if not, any correction I may need to do?

d1=(0:5);
d2=(0:5);
dtdnn_net=distdelaynet({d1,d2},5);
[p,Pi,Ai,t]=preparets(dtdnn_net,y2,y2);
dtdnn_net.trainFcn='trainbr';
dtdnn_net.trainParam.epochs=1000;
dtdnn_net.trainParam.lr=0.01;
dtdnn_net.layers{2}.size=1;
dtdnn_net=train(dtdnn_net,p,t);
yp=sim(dtdnn_net,p);

Thank you

[Greg Heath]

Why did you post this without testing it on the data indicated in

 help distdelaynet

?

Why didn't you use

 view(net)

as indicated in the help and doc documentation?

 doc distdelaynet

Why didn't you calculate the resulting resubstitution error?

Answer 1

[Greg Heath]

 [P,T] = simpleseries_dataset; 
d1=(0:5);
d2=(0:5);
dtdnn_net    = distdelaynet({d1,d2},5);
[Ps,Pi,Ai,Ts] = preparets(dtdnn_net,P,T);  
ts     = cell2mat(Ts);
MSE00s = var(ts',1)         %  0.042348 
dtdnn_net.trainFcn          = 'trainbr';
dtdnn_net.trainParam.epochs = 1000;
dtdnn_net.trainParam.lr     = 0.01;
dtdnn_net.layers{2}.size    = 1;
rng('default')                                             
[dtdnn_net tr Ys Es Xf Af]  = train(dtdnn_net,Ps,Ts,Pi,Ai);  
view(dtdnn_net) 
%Ys = sim(dtdnn_net,Ps,Pi,Ai);    
%Es = gsubtract(dtdnn_net,Ts,Ys) 

 es  = cell2mat(Es);
 R2s = 1-mse(es)/MSE00s     %  0.9996

NOTE: Using net defaults with trainlm decreases learning time by a factor of 20