Combinatorial Network of Dynamic Models: A Method to Improve Bad-quality Models

Abstract

This work proposes a combinatorial network of
dynamic models in order to combine bad-quality models to obtain
a better model. The combined model can be useful in situations
where a good model cannot be obtained from data, such when
there is a bad signal noise ratio in identification data-set or when
a specific input cannot be applied to generate identification data.
To do this, we present two different approaches: an analytical one
and a numerical method, both combining a weighted sum of the
bad quality models. The method is tested on models obtained
through a multiobjective system identification procedure, and
from models obtained through an interval system identification
procedure. The combined model has improved the performance
in the validation indexes analyzed, reaching a reduction up to
65% in the RMSE index, 95% in the MSE index of the static
curve, 87% in the energy of the residues vector and a reduction
of 21% in the autocorrelation energy of the residues vector.