Full Bayesian identification of linear dynamic systems using stable kernels.

ABSTRACT: System identification learns mathematical models of dynamic systems starting from input-output data. Despite its long history, such research area is still extremely active. New challenges are posed by identification of complex physical processes given by the interconnection of dynamic systems. Examples arise in biology and industry, e.g., in the study of brain dynamics or sensor networks. In the last years, regularized kernel-based identification, with inspiration from machine learning, has emerged as an interesting alternative to the classical approach commonly adopted in the literature. In the linear setting, it uses the class of stable kernels to include fundamental features of physical dynamical systems, e.g., smooth exponential decay of impulse responses. Such class includes also unkn

SUBMITTER: Pillonetto G 

PROVIDER: S-EPMC10161125 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature