Neural networks for data prediction

Hello!

As I am not advanced user of Matlab, maybe this is a stupid question for you.

Basically I have 6 experiments; each experiment has 9 parameters (x1,x2…x9) and one output value (Y).

Experiment matrix is following:

 #   x1  x2  x3  x4  x5  x6  x7  x8  x9   Y
 1
 2
 3
 4
 5
 6

All x parameters and Y parameter are experimental, and I want to use neural networks to build a relationship between all x and Y (e.g. formula that would consist of all x parameters, which would bring to Y parameter as close as possible.) And build a code, so that when I enter new x values, it would predict Y value.

I would appreciate any tips, how to get it done.

Thanks and best regards, Vitaly

[Greg Heath]

Please look up the definitions of variable and parameter.

Answer 1

[Greg Heath]

[ I N ] = size(input) % [ 9 6 ]

[ O N ] = size (target) % [ 1 6 ]

Neq = prod(size(target)) % No. of EQUATIONS = N*O = 6

% For a NN with I-H-O node topology, the No. of UNKNOWNS (weights and biases) is % Nw = (I+1)*H+(H+1)*O = I*H input layer weights, H*O output layer weights and H+O biases % Nw < Neq when H <= Hub where

Hub = -1 + ceil( (Neq-O)/(I+O+1) )% = 0 (A Linear model)

% However, for a linear NN with I-O node topology, the No. of UNKNOWNS (weights and biases) is % Nw = (I+1)*O % I*O = 9 weights + O = 1 bias = 10 > Neq = 6 equations (underdetermined system)

% For a robust design Neq >> Nw (a VERY over determined system) is desired. Therefore, consider

 1. Much more data
 2. Fewer input variables ( e.g., help/doc STEPWISEFIT)
 3. BACKSLASH or PINV for an undetermined linear system
 3. Regularization (e.g., help/doc TRAINBR) . 

 help fitnet
 doc fitnet

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 greg fitnet

Hope this helps

Thank you for officially accepting my answer

Greg