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Found 8 papers, 3 papers with code
Baseline Results for Selected Nonlinear System Identification Benchmarks
Nonlinear system identification remains an important open challenge across research and academia.
State Derivative Normalization for Continuous-Time Deep Neural Networks
However, in continuous-time state-space model estimation, it has been observed that improper normalization of either the hidden state or hidden state derivative of the model estimate, or even of the time interval can lead to numerical and optimization challenges with deep learning based methods.
Meta-State-Space Learning: An Identification Approach for Stochastic Dynamical Systems
Available methods for identification of stochastic dynamical systems from input-output data generally impose restricting structural assumptions on either the noise structure in the data-generating system or the possible state probability distributions.
Initialization Approach for Nonlinear State-Space Identification via the Subspace Encoder Approach
The SUBNET neural network architecture has been developed to identify nonlinear state-space models from input-output data.
Computationally efficient predictive control based on ANN state-space models
Artificial neural networks (ANN) have been shown to be flexible and effective function estimators for identification of nonlinear state-space models.
Deep Subspace Encoders for Nonlinear System Identification
In this paper, we aim to overcome these issues by proposing a method which uses a truncated prediction loss and a subspace encoder for state estimation.
Continuous-time identification of dynamic state-space models by deep subspace encoding
Continuous-time (CT) modeling has proven to provide improved sample efficiency and interpretability in learning the dynamical behavior of physical systems compared to discrete-time (DT) models.
Deep-Learning-Based Identification of LPV Models for Nonlinear Systems
The Linear Parameter-Varying (LPV) framework provides a modeling and control design toolchain to address nonlinear (NL) system behavior via linear surrogate models.
