Investigation of static curve information for multiobjective system identification

Abstract

System Identification is an area of knowledge that aims at finding models by means of observations. Dynamic input-output data are usually collected in order to select structure and estimate parameters of an specific mathematical representation. In this case, a mono-objective optimization is employed, as the Least Square Method, in order to maximize the prediction accuracy. More recently, multiobjective optimization has been used for system identification, taking in account auxiliary information, such as fixed points and static curve, which is usually used to increase the global validity. It is stated in literature that there is a tradeoff between global validity and prediction accuracy (Aguirre et al., IEEE Trans. Circuits Syst., 40,[1081][1082][1083][1084][1085] 2000). In this paper, it is shown that the tradeoff between objectives in a multiobjective system identification depends on the decision criterion. In fact, using the Euclidean norm of a bi-objective system identification in two real systems, as the amount of auxiliary information increases, the prediction accuracy behaves irregularly. This suggests an increment of importance for criterion decision in a MSI.