Joint Learning of Linear Time-Invariant Dynamical Systems

Understand

Linear time-invariant systems are very popular models in system theory and applications.

• A fundamental problem in system identification that remains rather unaddressed in extant literature is to leverage commonalities amongst related linear systems to estimate their transition matrices more accurately.
• To address this problem, the current paper investigates methods for jointly estimating the transition matrices of multiple systems.
• It is assumed that the transition matrices are unknown linear functions of some unknown shared basis matrices.