Partial differential equations numerical modeling using dynamic neural networks

Rita Fuentes, Alexander Poznyak, Isaac Chairez, Tatyana Poznyak

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferencia

Resumen

In this paper a strategy based on differential neural networks (DNN) for the identification of the parameters in a mathematical model described by partial differential equations is proposed. The identification problem is reduced to finding an exact expression for the weights dynamics using the DNNs properties. The adaptive laws for weights ensure the convergence of the DNN trajectories to the PDE states. To investigate the qualitative behavior of the suggested methodology, here the non parametric modeling problem for a distributed parameter plant is analyzed: the anaerobic digestion system © 2009 Springer Berlin Heidelberg.
Idioma originalInglés estadounidense
Título de la publicación alojadaPartial differential equations numerical modeling using dynamic neural networks
Páginas552-562
Número de páginas495
ISBN (versión digital)3642042767, 9783642042768
DOI
EstadoPublicada - 27 nov 2009
EventoLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duración: 1 ene 2014 → …

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen5769 LNCS
ISSN (versión impresa)0302-9743

Conferencia

ConferenciaLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Período1/01/14 → …

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