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

28 Feb 2018  ·  Ribeiro Rui Teixeira, Mauroy Alexandre, Goncalves Jorge ·

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. read more

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Systems and Control Dynamical Systems


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