Equivalence of non-linear model structures based on Pareto uncertainty

作     者：Barbosa, Alipio Monteiro Caldeira Takahashi, Ricardo Hiroshi Aguirre, Luis Antonio 

作者机构：Univ Fed Minas Gerais Programa Posgrad Engn Eletr BR-31270901 Belo Horizonte MG Brazil Univ Fed Minas Gerais Dept Matemat BR-31270901 Belo Horizonte MG Brazil 

出 版 物：《IET CONTROL THEORY AND APPLICATIONS》 (IET Control Theory Appl.)

年 卷 期：2015年第9卷第16期

页      面：2423-2429页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0804[工学-仪器科学与技术] 0811[工学-控制科学与工程] 

基　　金：CAPES CNPq 

主　　题：Pareto optimisation nonlinear systems statistical testing convex programming concave programming nonlinear model structure equivalence Pareto uncertainty biobjective optimisation hypothesis testing user defined confidence level argument convex optimisation nonconvex problems 

摘      要：In view of practical limitations, it is not always feasible to find the best model structure. In such situations, a more realistic problem to address seems to be the choice of a set of model structures that are not clearly distinguishable in view of the available data. This study proposes a procedure based on the bi-objective optimisation and hypothesis testing that, given a pool of candidate model structures, will select a subset that is consistent with the data given a user-defined confidence level. Such a subset carries an important information that no single most likely model structure can deliver: the unmodelled component of system behaviour, given the model structure uncertainty. The procedure is illustrated using simulated and measured data. For the sake of argument convex optimisation has been considered, although the procedure also applies to non-convex problems.