NARMAX model identification using a randomised approach

Página do item simplificado
dc.creatorPedro Felipe Leiter Retes
dc.creatorLuis Antonio Aguirre
dc.date.accessioned2025-05-21T14:33:01Z
dc.date.accessioned2025-09-08T23:39:55Z
dc.date.available2025-05-21T14:33:01Z
dc.date.issued2019
dc.identifier.doihttps://doi.org/10.1504/IJMIC.2019.098779
dc.identifier.issn1746-6172
dc.identifier.urihttps://hdl.handle.net/1843/82417
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofInternational journal of modelling, identification and control
dc.rightsAcesso Restrito
dc.subjectSistemas não lineares
dc.subject.otherNARMAX
dc.subject.otherNonlinear models
dc.subject.otherELS
dc.subject.otherOLS
dc.subject.otherRandomised algorithm for model structure selection
dc.subject.otherRaMSS
dc.subject.otherNARX
dc.subject.otherSystem identification
dc.subject.otherParameter estimation
dc.titleNARMAX model identification using a randomised approach
dc.typeArtigo de periódico
local.citation.issue3
local.citation.spage205
local.citation.volume31
local.description.resumoStructure selection is one of the most critical steps in nonlinear system identification. A large family of methods, based on model prediction error, use concepts and tools from linear algebra. Other methods, based on model simulation error, have to deal with non-convex optimisation problems. More recently a family of methods have been put forward that has probabilistic setting. The randomised algorithm for model structure selection (RaMSS) belongs to this family and it has been shown to be effective to select regressors for NARX models. In the present paper, such a method is extended to cope with NARMAX models. The performance of the proposed method is illustrated using simulated and experimental data. It is shown that the proposed method is capable of correctly selecting model structures from simulation data. The method was also applied to experimental data with successful results.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.initialsUFMG
local.url.externahttps://www.inderscienceonline.com/doi/abs/10.1504/IJMIC.2019.098779

Arquivos

Licença do pacote

Agora exibindo 1 - 1 de 1
Imagem de Miniatura
Nome:
License.txt
Tamanho:
1.99 KB
Formato:
Plain Text
Descrição:
Baixar

Coleções

Artigo de Periódico

Logo UFMG