Work

A Levenberg-Marquardt Algorithm for Sparse Identification of Dynamical Systems

Year: 2022

Type: article

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.

Source: IEEE Transactions on Neural Networks and Learning Systems

DOI: 10.1109/tnnls.2022.3157963

Authors: Mark Haring, Esten Ingar Grøtli, Signe Riemer-Sørensen, Katrine Seel, Kristian Gaustad Hanssen

Institutions: SINTEF, SINTEF Digital

Language: English

Topic: Control Systems and Identification

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,195

Funders: Norges Forskningsråd, Elkem