5 papers
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…
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…
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…
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^{…
Stable Linear System Identification with Prior Knowledge by Riemannian Sequential Quadratic Optimization
Mitsuaki Obara, Kazuhiro Sato, Hiroki Sakamoto +2
We consider an identification method for a linear continuous time-invariant autonomous system from noisy state observations. In particular, we focus on the identification to satisf…