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A Levenberg-Marquardt Algorithm for Sparse Identification of Dynamical Systems

Abstract

Low complexity of a system model is essential for its use in real-time applications. However, sparse identification methods commonly have stringent requirements that exclude them from being applied in an industrial setting. In this article, we introduce a flexible method for the sparse identification of dynamical systems described by ordinary differential equations. Our method relieves many of the requirements imposed by other methods that relate to the structure of the model and the dataset, such as fixed sampling rates, full state measurements, and linearity of the model. The Levenberg-Marquardt algorithm is used to solve the identification problem. We show that the Levenberg-Marquardt algorithm can be written in a form that enables parallel computing, which greatly diminishes the time required to solve the identification problem. An efficient backward elimination strategy is presented to construct a lean system model.
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Category

Academic article

Language

English

Affiliation

  • SINTEF Digital / Mathematics and Cybernetics
  • Norwegian University of Science and Technology

Year

2022

Published in

IEEE Transactions on Neural Networks and Learning Systems

ISSN

2162-237X

Volume

34

Issue

11

Page(s)

9323 - 9336

View this publication at Norwegian Research Information Repository