An adaptive neural identifier with applications to financial and welding systems

Gularte, Kevin Herman Muraro; Muñoz Chávez, Jairo José; Vargas, José Alfredo Ruiz; Alfaro, Sadek Crisóstomo Absi

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Campo DC	Valor	Lengua/Idioma
dc.contributor.author	Gularte, Kevin Herman Muraro	-
dc.contributor.author	Muñoz Chávez, Jairo José	-
dc.contributor.author	Vargas, José Alfredo Ruiz	-
dc.contributor.author	Alfaro, Sadek Crisóstomo Absi	-
dc.date.accessioned	2022-04-12T15:08:34Z	-
dc.date.available	2022-04-12T15:08:34Z	-
dc.date.issued	2021	-
dc.identifier.citation	GULARTE, Kevin Herman Muraro et al. An adaptive neural identifier with applications to financial and welding systems. International Journal of Control, Automation and Systems, v. 19, p. 1976–1987, 2021. DOI: https://doi.org/10.1007/s12555-020-0081-x. Disponível em: https://link.springer.com/article/10.1007/s12555-020-0081-x. Acesso em: 12 abr. 2022.	pt_BR
dc.identifier.uri	https://repositorio.unb.br/handle/10482/43379	-
dc.language.iso	Inglês	pt_BR
dc.publisher	Springer	pt_BR
dc.rights	Acesso Restrito	pt_BR
dc.title	An adaptive neural identifier with applications to financial and welding systems	pt_BR
dc.type	Artigo	pt_BR
dc.subject.keyword	Teoria de Lyapunov	pt_BR
dc.subject.keyword	Redes neurais (Computação)	pt_BR
dc.rights.license	©ICROS, KIEE and Springer 2021	pt_BR
dc.identifier.doi	https://doi.org/10.1007/s12555-020-0081-x	pt_BR
dc.relation.publisherversion	https://link.springer.com/article/10.1007/s12555-020-0081-x	pt_BR
dc.description.abstract1	This paper considers the online identification problem of uncertain systems. Based on parallel and series-parallel configurations with feedback and by using Lyapunov arguments, a unified identification algorithm is introduced to ensure the boundedness of all associated errors and convergence of the state estimation error to an arbitrary neighborhood of the origin. The main peculiarity of the proposed algorithm lies in allowing the adjustment of the identification transient by using parameters that are not related to the residual state error. Two examples are deemed to validate the theoretical results and show the relevance of the application of the proposed methodology for online weld geometry prediction.	pt_BR
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