dynoGP: Deep Gaussian Processes for dynamic system identification
This addresses the problem of system identification with uncertainty quantification for researchers and practitioners in control systems and machine learning, though it appears incremental as it builds on existing GP methods.
The paper tackled system identification for dynamical systems by proposing a novel approach using Deep Gaussian Processes that combine linear dynamic and static GPs, resulting in a framework that includes uncertainty quantification and demonstrated effectiveness on simulated and real-world data.
In this work, we present a novel approach to system identification for dynamical systems, based on a specific class of Deep Gaussian Processes (Deep GPs). 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.