Results 1 to 10 of about 1,828,697 (335)
Nonlinear System Identification: A User-Oriented Road Map [PDF]
IEEE Control Systems, 2019 122 pages, 59 ...
Johan Schoukens, Lennart Ljung
semanticscholar +6 more sources
SINDy-SA framework: enhancing nonlinear system identification with sensitivity analysis. [PDF]
Nonlinear Dyn, 2022 Machine learning methods have revolutionized studies in several areas of knowledge, helping to understand and extract information from experimental data.
Naozuka GT +3 more
europepmc +2 more sources
Equation Discovery for Nonlinear System Identification [PDF]
IEEE Access, 2020 Equation discovery methods enable modelers to combine domain-specific knowledge and system identification to construct models most suitable for a selected modeling task.
Nikola Simidjievski +3 more
doaj +2 more sources
On the smoothness of nonlinear system identification [PDF]
at - Automatisierungstechnik, 2020 We shed new light on the \textit{smoothness} of optimization problems arising in prediction error parameter estimation of linear and nonlinear systems.
Aguirre, Luis A. +4 more
core +2 more sources
How Entropic Regression Beats the Outliers Problem in Nonlinear System Identification [PDF]
Chaos, 2019 In this work, we developed a nonlinear System Identification (SID) method that we called Entropic Regression. Our method adopts an information-theoretic measure for the data-driven discovery of the underlying dynamics.
AlMomani, Abd AlRahman R. +2 more
core +2 more sources
Nonlinear System Identification
Measurement Science and Technology, 2002 This is a comprehensive book discussing several methods for the identification of nonlinear systems. Identification is extremely relevant in applications and only recently has much ongoing research addressed the pressing problem of identifying systems with nonlinearities.
L. Ljung
semanticscholar +3 more sources
Deep Subspace Encoders for Nonlinear System Identification [PDF]
at - Automatisierungstechnik, 2022 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.
G. Beintema, M. Schoukens, R. T'oth
semanticscholar +1 more source
Neural Ordinary Differential Equations for Nonlinear System Identification [PDF]
American Control Conference, 2022 Neural ordinary differential equations (NODE) have been recently proposed as a promising approach for nonlinear system identification tasks. In this work, we systematically compare their predictive performance with current state-of-the-art nonlinear and ...
Aowabin Rahman +3 more
semanticscholar +1 more source
Deep State Space Models for Nonlinear System Identification [PDF]
IFAC-PapersOnLine, 2020 An actively evolving model class for generative temporal models developed in the deep learning community are deep state space models (SSMs) which have a close connection to classic SSMs.
Daniel Gedon +3 more
semanticscholar +1 more source