用深度高斯过程建模动态系统，同时给出不确定性估计。
dynoGP: Deep Gaussian Processes for dynamic system identification
- 将线性动态高斯过程与静态非线性高斯过程级联建模系统
- 在模拟和真实数据上均实现高精度系统辨识与不确定性量化
- 适合需要可信预测的工业控制与物理建模场景
本文提出一种基于深度高斯过程（Deep GPs）的新方法，用于动态系统辨识。该模型通过连接线性动态高斯过程（等价于随机线性时不变系统）和静态高斯过程（用于建模静态非线性），构建可解释的系统结构。方法融合了数据驱动模型（如神经网络）的灵活性与概率输出能力，提供包含不确定性量化的完整系统辨识框架。我们在模拟数据和真实世界数据上验证了该方法的有效性，展示了其在复杂动态系统建模中的潜力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。