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In this work, we address the problem of identifying sparse continuous-timedynamical systems when the spacing between successive samples (the samplingperiod) is not constant over time. The proposed approach combines theleave-one-sample-out cross-validation error trick from machine learning with aniterative subset growth method to select the subset of basis functions thatgoverns the dynamics of the system. The least-squares solution using only theselected subset of basis functions is then used. The approach is illustrated ontwo examples: a 6-node feedback ring and the Van der Pol oscillator.

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