how to make a neural network with a large matrix as inputs?
2 vues (au cours des 30 derniers jours)
Afficher commentaires plus anciens
this is the code:
Ptr=xlsread('liaa(16s).xlsx','sheet1','I3:I402');
T=xlsread('liaa(16s).xlsx','sheet1','G3:G402');
net=newff(Ptr,T,4,{'logsig','purelin'},'trainlm','learngdm');
[net,tr]=train(net,Ptr,T);
y=sim(net,Ptr)
plot(Ptr,T,'bo',Ptr,y,'r*');
title('Perbandingan antara Target (o) dan Output Jaringan (*)');
xlabel('input');
and the errors said:
??? Error using ==> plus
Matrix dimensions must agree.
Error in ==> calcperf2 at 163
N{i,ts} = N{i,ts} + Z{k};
Error in ==> trainlm at 253
[perf,El,trainV.Y,Ac,N,Zb,Zi,Zl] = calcperf2(net,X,trainV.Pd,trainV.Tl,trainV.Ai,Q,TS);
Error in ==> network.train at 219
[net,tr] = feval(net.trainFcn,net,tr,trainV,valV,testV);
Error in ==> training_lia_vt at 11
[net,tr]=train(net,Ptr,T);
Both Ptr and T are 400x1 matrix, and i couldn't get the program to run unless the matrix get transposed. but if transpose the matrix the results i get is reversed. i'm supposed to get a graphic like this one:
but instead the result is:
0 commentaires
Réponse acceptée
Greg Heath
le 14 Oct 2014
1. The matrices should be transposed.
2. there are 5 relevant plots
a. input vs time
b. target vs time
c. target vs input
d. output superimposed on b
e. output superimposed on c
Hope this helps.
Thank you for formally accepting my answer
Greg
2 commentaires
Greg Heath
le 17 Oct 2014
1x400 is correct. If your results are backwards use fliplr.
You can plot your results any way you want.
Greg
Plus de réponses (0)
Voir également
Catégories
En savoir plus sur Define Shallow Neural Network Architectures dans Help Center et File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!