dynoGP: Deep Gaussian Processes for Dynamic System Identification

Abstract

ABSTRACT In this work, we present a novel approach to system identification for dynamical systems, based on a specific class of deep Gaussian processes (deepGPs). These models are constructed by interconnecting linear dynamic GPs (equivalent to stochastic linear time‐invariant dynamical systems) and static GPs (to model static nonlinearities). Our approach combines the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution. This offers a more comprehensive framework for system identification that includes uncertainty quantification. Using both simulated and real‐world data, we demonstrate the effectiveness of the proposed approach.