Online learning of neural state-space models

• URL: http://arxiv.org/abs/2607.17614v1
• Date: Mon, 20 Jul 2026 07:04:15 GMT
• Title: Online learning of neural state-space models
• Abstract summary: Recent advances in deep-learning-based nonlinear system identification have led to encoder-based estimation of neural state-space (ANN-SS) models.<n>This paper presents a batch-wise learning pipeline and a direct recursion identification algorithm for subspace encoder-based ANN-SS models.
• Score: 1.9377646956063705
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 results demonstrate that the proposed approach enables computationally efficient online adaptation with high model accuracy.

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