Nonlinear system identification via sparse Bayesian regression based on collaborative neurodynamic optimization

Author:

Okunev Alexey1,Burnaev Evgeny2ORCID

Affiliation:

1. 366033 Skolkovo Institute of Science and Technology , 121205 Moscow , Russia

2. Skolkovo Institute of Science and Technology , 121205; and Autonomous Non-Profit Organization Artificial Intelligence Research Institute (AIRI), 105064 Moscow , Russia

Abstract

Abstract Sparse identification of nonlinear dynamics is a popular approach to system identification. In this approach system identification is reformulated as a sparse regression problem, and the use of a good sparse regression method is crucial. Sparse Bayesian learning based on collaborative neurodynamic optimization is a recent method that consistently produces high-quality solutions. In this article, we extensively assess how this method performs for ordinary differential equation identification. We find that it works very well compared with sparse regression algorithms currently used for this task in terms of the tradeoff between the approximation accuracy and the complexity of the identified system. We also propose a way to substantially reduce the computational complexity of this algorithm compared with its original implementation, thus making it even more practical.

Funder

Russian Science Foundation

Publisher

Walter de Gruyter GmbH

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