4 papers
A Deep State-Space Model Compression Method using Upper Bound on Output Error
Hiroki Sakamoto, Kazuhiro Sato
We study deep state-space models (Deep SSMs) that contain linear quadratic-output (LQO) systems as internal blocks and present a compression method with a provable output error gua…
Data-Driven Regularized Time-Limited h2 Model Reduction from Noisy Impulse Responses
Hiroki Sakamoto, Kazuhiro Sato
This paper develops a data-driven time-limited h2 model reduction method for discrete-time linear time-invariant systems. Specifically, we formulate and solve a regularized time-li…
Data-driven h2 model reduction for linear discrete-time systems
Hiroki Sakamoto, Kazuhiro Sato
We present a data-driven framework for -optimal model reduction for linear discrete-time systems. Our main contribution is to create optimal reduced-order models in the $h^{…
Compression Method for Deep Diagonal State Space Model Based on Optimal Reduction
Hiroki Sakamoto, Kazuhiro Sato
Deep learning models incorporating linear SSMs have gained attention for capturing long-range dependencies in sequential data. However, their large parameter sizes pose challenges…