Creating a Neural Network for Classification in Matlab - matlab

Question

I am trying to do classification using neural network and I have written the following code. Is this the code required to perform the training and classification?
%n1 to s5(n1=147,n2=205,n3=166,n4=204,n5=167,b1=156,b2=172,b3=153,b4=151,b5=160,r1=133,r2=135,r3=190,r4=143,ru1=133,ru2=153,ru3=154,ru4=137,s1=132,165,130,136,148)
%code:
T = [n1,n2,n3,n4,n5,b1,b2,b3,b4,b5,r1,r2,r3,r4,r5,ru1,ru2,ru3,ru4,s1,s2,s3,s4,s5];
x = [0 0 0 0 0 1 1 1 1 1 2 2 2 2 2 3 3 3 3 4 4 4 4 4];
net1 = newff(minmax(T),[30 20 1],{'logsig','logsig','purelin'},'trainrp');
net1.trainParam.show = 1000;
net1.trainParam.lr = 0.04;
net1.trainParam.epochs = 7000;
net1.trainParam.goal = 1e-5;
[net1] = train(net1,T,x);
save net1 net1
Additionally, if I have more samples with more features then how should I represent it in T and X? How do I write T and x? For example:
sample 1 ..... 123 0.56 78 127 .......0
sample 2 .......127 0.89 56 132 ........0
sample3...... 134 0.72 65 140...1
sample4 156 0.55 69 145 .....1
sample 5 112 0.10 12 120 .......2
sample 6 123 0.15 24 99 .......2
sample 7 95 0.32 98 198 ....3
sample 8 90 0.45 90 200...... 3

Answer 1

Yes your solution appears correct. I should note, newff is obsolete as of R2010b NNET 7.0 and it's last used in R2010a NNET 6.0.4. The recommended function is now is feedforwardnet. The feedforwardnet implementation should be as follows
net = feedforwardnet([30 20]);
net.layers{1:2}.transferFcn = 'logsig'
net.trainParam.show = 1000;
net.trainParam.lr = 0.04;
net.trainParam.epochs = 7000;
net.trainParam.goal = 1e-5;
[net1] = train(net1,T,x);
save net1 net1
I'm not quite sure what you're trying to do with minmax but that should be the basic structure of the feedforward NN.
To structure T and x, say you have the following data:
[123 0.56 78 127] belongs to class 1
[127 0.89 56 132] belongs to class 1
[134 0.72 65 140] belongs to class 2
[156 0.55 69 145] belongs to class 2
[112 0.10 12 120] belongs to class 3
[123 0.15 24 99] belongs to class 3
You can set T and x as follows:
T = [ 123 0.56 78 127; 127 0.89 56 132; 134 0.72 65 140; 156 0.55 69 145; 112 0.10 12 120; 123 0.15 24 99];
x = [1; 1; 2; 2; 3; 3];