TY - GEN
T1 - Neural numerical modeling for uncertain distributed parameter systems
AU - Fuentes, R.
AU - Poznyak, A.
AU - Chairez, I.
AU - Poznyak, T.
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=70449339458&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2009.5178909
DO - 10.1109/IJCNN.2009.5178909
M3 - Contribución a la conferencia
AN - SCOPUS:70449339458
SN - 9781424435531
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 909
EP - 916
BT - 2009 International Joint Conference on Neural Networks, IJCNN 2009
T2 - 2009 International Joint Conference on Neural Networks, IJCNN 2009
Y2 - 14 June 2009 through 19 June 2009
ER -