Neural Network: Blast Furnace Simulation -How do I how many neurons/layers i should use to optimize my network?

I´m sort of a new to neural networks and need some help. I´m working on a project that simulates a blast furnace and forcasts the silicon content of the molten metal output. I´ve tried many different combinations, sadly i haven´t had any real breakthrough. The best combination so far, that still doesn´t return satsfying results, is a 2 layer network:

180 neurons - TANSIG

1 neuron - PURELIN

Training = LM

I have 11 vectors of 1000 elements as input, some range from 0-10 some from 1300-1500. The target vector is the same size ranging from 0-3.

Any ideas on what I should do?

Answer 1

[Greg Heath]

INCORRECT: The target vector does not have the same size.

Standardize input and target matrices (ZSCORE or MAPSTD)

[I N ] = size(input) % [11 1000 ] [O N ] =size(target) % [ 1 1000 ]

Plot the output vs each input

Use the plot, CORRCOEF and REGRESS for indications of insignificant inputs.

 Ntrn = N - 2*(0.15*N) % 700 Neural Network default 
 Ntrneq = Ntrn*O       % 700 No. of Design equations% 
 Nw=(I+1)*H+(H+1)*O = 13*H + 1 unknown weights for I-H-O net

% Hub = -1+ceil( (Ntrneq-O)/(I+O+1) ) % 53 upperbound for Ntrneq >= Nw

First try to find a good value for H. Try

Ntrials = 10% random initial weight trials for each of the values of H in the range

 Hmin=0
 dH = 5
 Hmax =50
 j = 0
 for h = Hmin:dH:Hmax 
     j = j+1
     if h == 0
        net = fitmet([]);
     else 
        net = fitnet(h);
     end
     for i = 1:Ntrials
 % --------SNIP
     end
 end

Choose the smallest value of h that yields acceptable validation set results.

Hope this helps.

Thank you for formally accepting my answer

Greg