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

@article{Ribeiro2018AnAT, title={An Approach to Sparse Continuous-time System Identification from Unevenly Sampled Data}, author={Rui Teixeira Ribeiro and Alexandre Mauroy and Jorge M. GonΓ§alves}, journal={ArXiv}, year={2018}, volume={abs/1802.10348} }

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 thenβ¦Β

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