Online learning of neural state-space models
Recent advances in deep-learning-based nonlinear system identification have led to encoder-based estimation of neural state-space (ANN-SS) models that achieve state-of-the-art performance in offline settings by estimating initial model states from past input-output data. These methods are typically used in multiple-shooting-based offline identification, and online learning of these models remains largely unexplored. This paper presents a batch-wise learning pipeline and a direct recursive identification algorithm for subspace encoder-based ANN-SS models. We provide convergence analysis of the recursive formulation and validate its performance through extensive simulation studies. The resu...
| Authors | Bendegúz Györök, Tamás Péni, Maarten Schoukens et al. |
|---|---|
| Primary category | eess.SY |
| Published | 20 July 2026 |
| Last updated at source | 13 October 2026 |
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