发表机构
Delft University of Technology（代尔夫特理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出两种分布式递归高斯过程算法ADMM-RGP和PDMM-RGP，用于多智能体系统中的多输出回归，通过参数选择加速收敛以减少通信，并在真实风场数据上验证了其高效性和准确性。
AI 中文摘要
高斯过程（GPs）为从噪声测量中学习未知函数同时量化预测不确定性提供了一个灵活的框架，使其非常适合多智能体系统中的估计任务。然而，当测量数据由多个智能体收集时，在不进行集中处理的情况下维护统一的高斯过程模型，需要能够利用局部测量和与邻居智能体通信的高效分布式算法。在这项工作中，我们为多输出高斯过程回归开发了两种分布式递归高斯过程（RGP）算法：ADMM-RGP和PDMM-RGP。我们分析了这两种算法的稳定性和收敛性，并开发了参数选择策略以加速收敛，从而减少通信负担。所提出的方法在一个真实世界的多输出风场数据集上进行了验证，并在具有不同连通性的通信图上检查了其收敛行为。数值实验表明，与现有技术相比，ADMM-RGP和PDMM-RGP可以显著减少通信，同时保持相当的估计精度和网络范围的共识。
英文摘要
Gaussian processes (GPs) provide a flexible framework for learning unknown functions from noisy measurements while quantifying predictive uncertainty, making them well suited for estimation in multi-agent systems. However, when measurements are collected by multiple agents, maintaining a unified GP model without centralized processing requires efficient distributed algorithms that can operate using local measurements and communication with neighboring agents. In this work, we develop two distributed recursive GP (RGP) algorithms for multi-output GP regression: ADMM-RGP and PDMM-RGP. We analyze the stability and convergence of both algorithms and develop parameter selection strategies to accelerate convergence, thus reducing the communication burden. The proposed methods are validated on a real-world multi-output wind dataset, and their convergence behavior is examined across communication graphs with varying connectivity. Numerical experiments demonstrate that ADMM-RGP and PDMM-RGP can significantly reduce communication relative to the state of the art, while maintaining comparable estimation accuracy and network-wide consensus.
