Meta-state-space learning: an identification approach for stochastic dynamical systems
From MaRDI portal
Publication:6585435
DOI10.1016/j.automatica.2024.111787zbMATH Open1542.934MaRDI QIDQ6585435
Roland Tóth, Gerben I. Beintema, Maarten Schoukens
Publication date: 9 August 2024
Published in: Automatica (Search for Journal in Brave)
Artificial neural networks and deep learning (68T07) Nonlinear systems in control theory (93C10) Identification in stochastic control theory (93E12)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- System identification of nonlinear state-space models
- Subspace identification of bilinear and LPV systems for open- and closed-loop data
- 4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems
- Identification of block-oriented nonlinear systems starting from linear approximations: a survey
- Maximum likelihood identification of Wiener models
- Stochastic Model Predictive Control: An Overview and Perspectives for Future Research
- Subspace model identification Part 2. Analysis of the elementary output-error state-space model identification algorithm
- Nonlinear filtering: Interacting particle resolution
- Nonlinear System Identification: A User-Oriented Road Map
- A New Kernel-Based Approach for NonlinearSystem Identification
- Automated multi-objective system identification using grammar-based genetic programming
- Deep subspace encoders for nonlinear system identification
This page was built for publication: Meta-state-space learning: an identification approach for stochastic dynamical systems