Lacerda, Márcio Júnior and Martins, Samir Angelo Milani and Nepomuceno, Erivelton (2018) Structure selection based on interval predictor model for recovering static non‐linearities from chaotic data. IET Control Theory & Applications, 12 (13). pp. 1889-1894. ISSN 1751-8652

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Abstract

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.

Item Type:	Article
Keywords:	Structure; selection based; interval predictor model; recovering; static; non-linearities; chaotic data;
Academic Unit:	Faculty of Science and Engineering > Electronic Engineering Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	16751
Identification Number:	https://doi.org/10.1049/iet-cta.2017.1033
Depositing User:	Erivelton Nepomuceno
Date Deposited:	28 Nov 2022 15:39
Journal or Publication Title:	IET Control Theory & Applications
Publisher:	Institution of Engineering and Technology (IET)
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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