Neural network for multiple input and multi output (MIMO) systems

I want to build a neural network for a multi input and multi output (MIMO) system described as:

    y1(t)= f1( x1(t), x2(t),...xn(t))
    y2(t)= f2( x1(t), x2(t),...xn(t))
    .....
    .....
    ym(t)= fm( x1(t), x2(t),...xn(t))

For single input single output system, mostly for function approximation of the form `y= f(t)`, where the neural network is trained for input t (independent variable) and output y, there are many examples. However, how do I construct or solve the MIMO problem ? How to I transform or represent the input or outputs to solve the problem with the matlab neural network toolbox?

Answer 1

[Greg Heath]

The typical NN is a MIMO function and the typical NNTBX design uses I-dimensional inputs

[ I N ] = size(input)

and O-dimensional output targets

[ O N ] = size(target)

Interpret all variables as rows of input and output matrices

Hope this helps.

Thank you for formally accepting my answer.

Greg

Answer 2

[Shashank Prasanna]

create different networks for each fi. fi: x1..xn -> yi

Also can you provide a little context to the questions? What is this for and where will you be using it?