Year: 2024
Type: article
Abstract: In this article, we study a class of stochastic nonlinear dynamical networks governed by coupling functions, showing that under certain assumptions, these networks can produce geometrically ergodic trajectories. Our findings suggest that a wide range of coupling functions can be effectively learned from just one sample trajectory in the network. This approach is practical, as it often aligns with the preference in many applications to conduct a single, extended experiment rather than repeating the same experiment under different initial conditions. Drawing on concentration inequalities for geometrically ergodic Markov chains, we present several results regarding the empirical estimator's convergence to the actual coupling function, substantiated by extensive simulations.
Authors: Arash Amini, Qiyu Sun, Nader Motee
Institutions: Lehigh University, University of Central Florida
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Office of Naval Research
Award: G3492737734
OpenAlex ID: W4402570579