  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.
