Neural numerical modeling for uncertain distributed parameter systems

R. Fuentes, A. Poznyak, I. Chairez, T. Poznyak

Research output: Contribution to conferencePaper

12 Scopus citations

Abstract

In this paper a strategy based on differential neural networks 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 differential neural networks properties. The adaptive laws for weights ensure the convergence of the neural network trajectories to the partial differential equation states. To investigate the qualitative behavior of the suggested methodology, here the non parametric modeling problem for a distributed parameter plant is analyzed: the tubular reactor system. © 2009 IEEE.
Original languageAmerican English
Pages909-916
Number of pages817
DOIs
StatePublished - 18 Nov 2009
EventProceedings of the International Joint Conference on Neural Networks -
Duration: 1 Dec 2010 → …

Conference

ConferenceProceedings of the International Joint Conference on Neural Networks
Period1/12/10 → …

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Fuentes, R., Poznyak, A., Chairez, I., & Poznyak, T. (2009). Neural numerical modeling for uncertain distributed parameter systems. 909-916. Paper presented at Proceedings of the International Joint Conference on Neural Networks, . https://doi.org/10.1109/IJCNN.2009.5178909