Problem
Research questions and friction points this paper is trying to address.
distributed sensor networks
partial state observations
unknown dynamics
Gaussian Process regression
COIN-GP: Cooperative Online Learning in Networked Distributed Systems with Partial Measurements via Gaussian Process Regression
🤖 AI Summary
本文解决了分布式网络系统中状态和部分未知动态的联合估计问题，通过基于观测器的动态合作学习框架结合在线分布式高斯过程回归方法来解决。
📝 Abstract
In this paper, we tackle the problem of jointly estimating the system states and partially unknown dynamics within distributed sensor-equipped networks, particularly in scenarios where only partial state observations are available. To address this issue, we propose an observer-based dynamic cooperative learning framework incorporating online distributed Gaussian Process (GP) regression, which enables accurate estimation despite incomplete in measurements and deficient GP models. In addition, a novel data collection strategy is introduced, with theoretical conditions ensuring feasible data acquisition. Moreover, we also derive an error upper bound encompassing state estimation and model estimation, leveraging the deterministic error bounds of GPs. Empirical simulations demonstrate the superiority of our approach compared to existing distributed GP-based methods.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Cooperative Online Learning
Gaussian Process Regression
Distributed Systems
Partial State Observations
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