1 research outputs found
Identification of Sparse Continuous-Time Linear Systems with Low Sampling Rate: Optimization Approaches
This paper addresses identification of sparse linear and noise-driven
continuous-time state-space systems, i.e., the right-hand sides in the
dynamical equations depend only on a subset of the states. The key assumption
in this study, is that the sample rate is not high enough to directly infer the
continuous time system from the data. This assumption is relevant in
applications where sampling is expensive or requires human intervention (e.g.,
biomedicine applications). We propose an iterative optimization scheme with
-regularization, where the search directions are restricted those that
decrease prediction error in each iteration. We provide numerical examples
illustrating the proposed method; the method outperforms the least squares
estimation for large noise.Comment: It has been merged into the arXiv article 1605.08590. No longer
needed to keep it. And it's not well prepare