On the smoothness of nonlinear system identification

DOI10.1016/J.AUTOMATICA.2020.109158zbMath1448.93064arXiv1905.00820OpenAlexW2943507526MaRDI QIDQ2003795

Koen Tiels, Jack Umenberger, Antônio H. Ribeiro, Thomas B. Schön, Luis Antonio Aguirre

Publication date: 5 October 2020

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1905.00820

zbMATH Keywords

system identificationparameter estimationprediction error methodsmultiple shootingoutput error models

Mathematics Subject Classification ID

System identification (93B30) Nonlinear systems in control theory (93C10)

Related Items (4)

Training recurrent neural networks by sequential least squares and the alternating direction method of multipliers ⋮ Deep subspace encoders for nonlinear system identification ⋮ Continuous-time system identification with neural networks: model structures and fitting criteria ⋮ Variational system identification for nonlinear state-space models

Uses Software

• SciPy
• Python

Cites Work

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• Simple mathematical models with very complicated dynamics

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