mlsanityData to 14 Oct 2026

Continuous-time system identification with neural networks: Model structures and fitting criteria

arXiv 2006.02915 ↗First posted 3 Jun 2020eess.SY cs.LG cs.SYcited by 30

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Abstract (arXiv)

This paper presents tailor-made neural model structures and two custom fitting criteria for learning dynamical systems. The proposed framework is based on a representation of the system behavior in terms of continuous-time state-space models. The sequence of hidden states is optimized along with the neural network parameters in order to minimize the difference between measured and estimated outputs, and at the same time to guarantee that the optimized state sequence is consistent with the estimated system dynamics. The effectiveness of the approach is demonstrated through three case studies, including two public system identification benchmarks based on experimental data.

Key facts. Cited by 30 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 Jun 2020.

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Citing papers per year: 2021: 2; 2022: 8; 2023: 6; 2024: 4; 2025: 3; 2026: 7.

30 papers.

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  1. 2022Learning a Single Near-hover Position Controller for Vastly Different Quadcopterscited by 21
  2. 2023From system models to class models: An in-context learning paradigmcited by 16
  3. 2022On the adaptation of recurrent neural networks for system identificationcited by 13
  4. 2022Deep Subspace Encoders for Nonlinear System Identificationcited by 13
  5. 2021Sparse Bayesian Deep Learning for Dynamic System Identificationcited by 9
  6. 2022Synthesis of Stabilizing Recurrent Equilibrium Network Controllerscited by 8
  7. 2022Few-Shot Learning by Dimensionality Reduction in Gradient Spacecited by 8
  8. 2024Baseline Results for Selected Nonlinear System Identification Benchmarkscited by 8
  9. 2023On the adaptation of in-context learners for system identificationcited by 7
  10. 2022Continuous-time identification of dynamic state-space models by deep subspace encodingcited by 6
  11. 2023Stable Linear Subspace Identification: A Machine Learning Approachcited by 4
  12. 2022On the effectiveness of Randomized Signatures as Reservoir for Learning Rough Dynamicscited by 3
18 more
  1. 2023ASV Station Keeping under Wind Disturbances using Neural Network Simulation Error Minimization Model Predictive Controlcited by 2
  2. 2025Manifold meta-learning for reduced-complexity neural system identificationcited by 2
  3. 2025Stable-by-Design Neural Network-Based LPV State-Space Models for System Identificationcited by 2
  4. 2026SOLIS: Physics-Informed Learning of Interpretable Neural Surrogates for Nonlinear Systemscited by 2
  5. 2021Deep learning with transfer functions: new applications in system identificationcited by 1
  6. 2022Likelihood-based generalization of Markov parameter estimation and multiple shooting objectives in system identificationcited by 1
  7. 2025A Physiologically-Constrained Neural Network Digital Twin Framework for Replicating Glucose Dynamics in Type 1 Diabetescited by 1
  8. 2026Tuning the burn-in phase in training recurrent neural networks improves their performancecited by 1
  9. 2026The Dynamic-Probabilistic Consistency Gap in Chaotic Surrogate Modelingcited by 1
  10. 2023Learning Flow Functions from Data with Applications to Nonlinear Oscillatorscited by 0
  11. 2023On the improvement of model-predictive controllerscited by 0
  12. 2024State Derivative Normalization for Continuous-Time Deep Neural Networkscited by 0
  13. 2024SINDy vs Hard Nonlinearities and Hidden Dynamics: a Benchmarking Studycited by 0
  14. 2024A neural network-based approach to hybrid systems identification for controlcited by 0
  15. 2026Beyond Prediction: Interval Neural Networks for Uncertainty-Aware System Identificationcited by 0
  16. 2026Online learning of neural state-space modelscited by 0
  17. 2026In-Context Learning for System Identification: Probabilistic Formulation, Long-Sequence Handling, and Benchmark Validationcited by 0
  18. 2026Scalable Identification of Physics-Based and Neural Dynamics from Partial Measurementscited by 0