This paper addresses the problem of structure
detection for polynomial NARX models. It develops MERR,
a multiobjective extension of a methodology well-known as
the error reduction ratio (ERR). It is shown that it is possible
to choose terms which take into account dynamics of prediction error and other types of affine information, such as
fixed points or static curve. Two examples are included to
illustrate the proposed methodology. A numerical example
shows that the technique is able to reconstruct the structure
of a system, known a priori. The identification of a pilot
DC–DC buck converter shows that the proposed approach is
capable to find models valid over a wide range of operation
points. In this latter example, MERR is compared with ERR
in two forms: (i) affine information is applied only in the
structure selection for MERR and (ii) affine information is
applied for structure selection for MERR and for parameter
estimation for both MERR and ERR. In both comparisons,
MERR presented nondominated solutions of Pareto set