Nonlinear system identification employing automatic differentiation

DOI10.1016/J.CNSNS.2013.02.017zbMath1306.93023arXiv1507.02543OpenAlexW2071426496WikidataQ48888385 ScholiaQ48888385MaRDI QIDQ2257423

Jan Schumann-Bischoff, Stefan Luther, Ulrich Parlitz

Publication date: 25 February 2015

Published in: Communications in Nonlinear Science and Numerical Simulation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1507.02543

zbMATH Keywords

parameter estimationdelay differential equationsdata assimilationnonlinear modelling

Mathematics Subject Classification ID

System identification (93B30)

Related Items (1)

Local observability of state variables and parameters in nonlinear modeling quantified by delay reconstruction

Uses Software

• sparseLM
• NumPy
• Python
• pyadolc
• GitHub
• Unnamed Item

Cites Work

• Effective actions for statistical data assimilation
• State and parameter estimation in nonlinear systems as an optimal tracking problem
• Automatic differentiation: techniques and applications
• Theory and computation of covariant Lyapunov vectors
• Dynamical State and Parameter Estimation
• An Algorithm for Least-Squares Estimation of Nonlinear Parameters
• Algorithm 755: ADOL-C
• Oscillation and Chaos in Physiological Control Systems
• NONLINEAR DYNAMICAL SYSTEM IDENTIFICATION FROM UNCERTAIN AND INDIRECT MEASUREMENTS
• Using synchronization of chaos to identify the dynamics of unknown systems
• Observers for Canonic Models of Neural Oscillators
• A method for the solution of certain non-linear problems in least squares

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