An Approach to Sparse Continuous-time System Identification from Unevenly Sampled Data

2018/02/28 by Rui Ribeiro, Ribeiro, Rui Teixeira, Alexandre Mauroy +3
Engineering · Physics and Astronomy · #Control Systems and Identification #Dynamical Systems (math.DS) #FOS: Electrical engineering #FOS: Mathematics #Fault Detection and Control Systems #Model Reduction and Neural Networks #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1802.10348

openalex publication_date 2018/02/28 · openalex created_date 2018/03/06 · openalex updated_date 2026/08/01

Abstract

In this work, we address the problem of identifying sparse continuous-time dynamical systems when the spacing between successive samples (the sampling period) is not constant over time. The proposed approach combines the leave-one-sample-out cross-validation error trick from machine learning with an iterative subset growth method to select the subset of basis functions that governs the dynamics of the system. The least-squares solution using only the selected subset of basis functions is then used. The approach is illustrated on two examples: a 6-node feedback ring and the Van der Pol oscillator.

Citations

• Systems Aliasing in Dynamic Network Reconstruction: Issues on Low Sampling Frequencies

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