• Corpus ID: 246210083

On the adaptation of recurrent neural networks for system identification

@article{Forgione2022OnTA,
  title={On the adaptation of recurrent neural networks for system identification},
  author={Marco Forgione and Aneri Muni and Dario Piga and Marco Gallieri},
  journal={ArXiv},
  year={2022},
  volume={abs/2201.08660}
}
This paper presents a transfer learning approach which enables fast and efficient adaptation of Recurrent Neural Network (RNN) models of dynamical systems. A nominal RNN model is first identified using available measurements. The system dynamics are then assumed to change, leading to an unacceptable degradation of the nominal model performance on the perturbed system. To cope with the mismatch, the model is augmented with an additive correction term trained on fresh data from the new dynamic… 

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