dynoGP: Deep Gaussian Processes for dynamic system identification

• URL: http://arxiv.org/abs/2502.05620v1
• Date: Sat, 08 Feb 2025 15:57:59 GMT
• Title: dynoGP: Deep Gaussian Processes for dynamic system identification
• Authors: Alessio Benavoli, Dario Piga, Marco Forgione, Marco Zaffalon,
• Abstract summary: We present a novel approach to system identification for dynamical systems based on a specific class of Gaussian Deep Processes (Deep GPs) 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. Using both simulated and real-world data, we demonstrate the effectiveness of the proposed approach.
• Score: 0.7692572272935511
• License:
• Abstract: 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.

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