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Nonlinear system identification remains an important open challenge across research and academia. Large numbers of novel approaches are seen published each year, each presenting improvements or extensions to existing methods. It is natural, therefore
Externí odkaz:
http://arxiv.org/abs/2405.10779
Autor:
Weigand, Jonas, Beintema, Gerben I., Ulmen, Jonas, Görges, Daniel, Tóth, Roland, Schoukens, Maarten, Ruskowski, Martin
The importance of proper data normalization for deep neural networks is well known. 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 th
Externí odkaz:
http://arxiv.org/abs/2401.02902
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 distribution
Externí odkaz:
http://arxiv.org/abs/2307.06675
The SUBNET neural network architecture has been developed to identify nonlinear state-space models from input-output data. To achieve this, it combines the rolled-out nonlinear state-space equations and a state encoder function, both parameterised as
Externí odkaz:
http://arxiv.org/abs/2304.02119
Autor:
Hoekstra, Jan H., Cseppentő, Bence, Beintema, Gerben I., Schoukens, Maarten, Kollár, Zsolt, Tóth, Roland
Artificial neural networks (ANN) have been shown to be flexible and effective function estimators for identification of nonlinear state-space models. However, if the resulting models are used directly for nonlinear model predictive control (NMPC), th
Externí odkaz:
http://arxiv.org/abs/2303.17305
Using Artificial Neural Networks (ANN) for nonlinear system identification has proven to be a promising approach, but despite of all recent research efforts, many practical and theoretical problems still remain open. Specifically, noise handling and
Externí odkaz:
http://arxiv.org/abs/2210.14816
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. However, even with numerous recent developments, the CT
Externí odkaz:
http://arxiv.org/abs/2204.09405
This work presents a novel regularization method for the identification of Nonlinear Autoregressive eXogenous (NARX) models. The regularization method promotes the exponential decay of the influence of past input samples on the current model output.
Externí odkaz:
http://arxiv.org/abs/2204.05892
The Linear Parameter-Varying (LPV) framework provides a modeling and control design toolchain to address nonlinear (NL) system behavior via linear surrogate models. Despite major research effort on LPV data-driven modeling, a key shortcoming of the c
Externí odkaz:
http://arxiv.org/abs/2204.04060
The present paper treats the identification of nonlinear dynamical systems using Koopman-based deep state-space encoders. Through this method, the usual drawback of needing to choose a dictionary of lifting functions a priori is circumvented. The enc
Externí odkaz:
http://arxiv.org/abs/2110.02583