Abstract
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
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Fuentes, R., Poznyak, A., Chairez, I., Poznyak, T. (2009). Partial Differential Equations Numerical Modeling Using Dynamic Neural Networks. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_56
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DOI: https://doi.org/10.1007/978-3-642-04277-5_56
Publisher Name: Springer, Berlin, Heidelberg
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