Neural network-based parametric system identification: a review
From MaRDI portal
Publication:6063217
DOI10.1080/00207721.2023.2241957MaRDI QIDQ6063217
Unnamed Author, Yi-Fan Zhao, Unnamed Author
Publication date: 7 November 2023
Published in: International Journal of Systems Science (Search for Journal in Brave)
Granger causalitynonlinear system identificationnonstationary time seriesinterpretable deep learningrobust and adaptive modelling
Artificial neural networks and deep learning (68T07) System identification (93B30) Nonlinear systems in control theory (93C10)
Cites Work
- Unnamed Item
- Unnamed Item
- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- Tracking nonlinear correlation for complex dynamic systems using a windowed error reduction ratio method
- Multilayer feedforward networks are universal approximators
- Generalized multiscale radial basis function networks
- Identification and frequency domain analysis of non-stationary and nonlinear systems using time-varying NARMAX models
- Identification of nonlinear time-varying systems using an online sliding-window and common model structure selection (CMSS) approach with applications to EEG
- Nonlinear System Identification
- Model selection approaches for non-linear system identification: a review
- Neural Networks and Deep Learning
- Fast Adaptive Gradient RBF Networks For Online Learning of Nonstationary Time Series
- Sparse identification of nonlinear dynamics for model predictive control in the low-data limit
- Long term prediction of non-linear time series using multiresolution wavelet models
- Discrete time subharmonic modelling and analysis
This page was built for publication: Neural network-based parametric system identification: a review