Open AccessStructure selection based on interval predictor model for recovering static non‐linearities from chaotic data
Author(s) -
Lacerda Márcio Júnior,
Martins Samir Angelo Milani,
Nepomuceno Erivelton Geraldo
Publication year - 2018
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2017.1033
Subject(s) - chaotic , selection (genetic algorithm) , interval (graph theory) , polynomial , regular polygon , mathematical optimization , mathematics , model selection , control theory (sociology) , computer science , interval arithmetic , least squares function approximation , algorithm , artificial intelligence , statistics , mathematical analysis , geometry , control (management) , combinatorics , bounded function , estimator
This study introduces a method of structure selection based on interval predictor model (IPM) and sum of squares formulation. The main contribution is to provide polynomial identified models that can recover static non‐linearities from chaotic data. Moreover, the dynamical behaviour of the identified models is also examined in the structure selection by considering convex combinations of the polynomial functions that describe the IPM. Numerical experiments contemplating non‐linear maps borrowed from the literature are presented to illustrate the potential and efficacy of the proposed approach.