Continuous neural identifier for uncertain nonlinear systems with time delays in the input signal

Mariel Alfaro-Ponce, Amadeo Arguelles, Isaac Chairez

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

Time-delay systems have been succesfully used to represent complex dynamical systems. Indeed, time-delay is usually encountered as part of many real systems. Among others, biological and chemical plants have been modeled using Time-delay terms with better results than those models that do not consider them. However, getting those models represents a formidable effort and sometimes the results are not so satisfactory. On the other hand, no parametric modelling offer an alternative to obtain suitable and usable models. Continuous neural networks (CNN) have been considered as a real alternative to produce such no parametric representations. This article introduces the design of a specific class of no parametric model for uncertain Time-delay system based on CNN considering the so-called delayed learning laws. The convergence analysis as well as the learning laws are produced from a Lyapunov-Krasovskii functional. A numerical example regarding the human innmunodeficiency virus dynamical behavior is used to show the performance of the suggeted no parametric identifier based on CNN.

Idioma originalInglés
Título de la publicación alojada2013 International Joint Conference on Neural Networks, IJCNN 2013
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas100-107
Número de páginas8
ISBN (versión impresa)9781467361293
DOI
EstadoPublicada - 2013
Evento2013 International Joint Conference on Neural Networks, IJCNN 2013 - Dallas, TX, Estados Unidos
Duración: 4 ago. 20139 ago. 2013

Serie de la publicación

NombreProceedings of the International Joint Conference on Neural Networks

Conferencia

Conferencia2013 International Joint Conference on Neural Networks, IJCNN 2013
País/TerritorioEstados Unidos
CiudadDallas, TX
Período4/08/139/08/13

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