Model order reduction of deep structured state-space models: A system-theoretic approach

arXiv:2403.14833v1 Announce Type: new
Abstract: With a specific emphasis on control design objectives, achieving accurate system modeling with limited complexity is crucial in parametric system identification. The recently introduced deep structured state-space models (SSM), which feature linear dynamical blocks as key constituent components, offer high predictive performance. However, the learned representations often suffer from excessively large model orders, which render them unsuitable for control design purposes. The current paper addresses this challenge by means of system-theoretic model order reduction techniques …

abstract arxiv complexity components control cs.lg cs.sy design eess.sy feature however identification key linear modeling parametric performance predictive space state type