i'm relatively new to nn and am not sure if nftool is the right tool to use for this problem.
i have three time series that are are the inputs to a nonlinear system. in a perfect world, the output of this nonlinear system would be a constant time series. here is my attempt to use a neural network to model this nonlinear system (a representative example with noise):
the output of this appears to yield a time series similar to the target series within its abilities to make a constant out of the three input time series.
my question comes when i use this trained network on a similar set of inputs, for example if i add a constant to the first input:
i (naively) expected that i would get the same output as before, just shifted by 10 (i.e. what i just added to the first input), however, i get something else altogether.
am i doing something wrong, and/or is there a better way to use neural networks for nonlinear, multidimensional transfer function estimation?