Identification of Sparse Continuous-Time Linear Systems with Low Sampling Rate: Optimization Approaches

arXiv: Systems and Control, Volume abs/1605.09199, 2016.

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Abstract:

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. Thi...More

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