A new approach for combined model class selection and parameters learning for auto-regressive neural models

• URL: http://arxiv.org/abs/2601.17442v1
• Date: Sat, 24 Jan 2026 12:26:25 GMT
• Title: A new approach for combined model class selection and parameters learning for auto-regressive neural models
• Authors: Corrado Sgadari, Alessio La Bella, Marcello Farina,
• Abstract summary: This work focuses on a specific Recurrent Neural Networks (RNNs) family, i.e. Auto-Regressive with eXogenous inputs Echo State Networks (XENARSNs)<n>The method allows to simultaneously select the optimal model class and learn model parameters from data.<n>Results show the effectiveness of the approach in identifying parsimonious yet accurate models suitable for control applications.
• Score: 0.4779196219827507
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work introduces a novel approach for the joint selection of model structure and parameter learning for nonlinear dynamical systems identification. Focusing on a specific Recurrent Neural Networks (RNNs) family, i.e., Nonlinear Auto-Regressive with eXogenous inputs Echo State Networks (NARXESNs), the method allows to simultaneously select the optimal model class and learn model parameters from data through a new set-membership (SM) based procedure. The results show the effectiveness of the approach in identifying parsimonious yet accurate models suitable for control applications. Moreover, the proposed framework enables a robust training strategy that explicitly accounts for bounded measurement noise and enhances model robustness by allowing data-consistent evaluation of simulation performance during parameter learning, a process generally NP-hard for models with autoregressive components.

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