通过离线学习富有表达力的自由度，在部分已知非线性状态空间模型中实现高效在线推断与学习
Efficient Online Inference and Learning in Partially Known Nonlinear State-Space Models by Learning Expressive Degrees of Freedom Offline
AI总结：
针对部分已知非线性状态空间模型在线联合状态估计与系统辨识的收敛及计算难题，提出离线条件化 GP、将在线学习限制于表达性子空间并用粒子滤波推断的方法，且给出误差理论界与电池仿真验证。
摘要：
智能现实系统至关重要地依赖于有关其系统状态和不断变化的运行条件（例如由温度、位置、磨损或老化引起的变化）的富有表达力的信息。为提供这些信息，在线推断与学习试图同时执行状态估计和（部分）系统辨识。当前工作将定制估计方案与灵活的基于学习的模型相结合，但由于推断问题中存在许多自由度（即待确定的参数），会遭遇收敛问题和计算复杂性。为解决这些问题，我们提出一种对高度灵活的 Gaussian Process（GP，高斯过程）表述进行数据驱动离线条件化的流程，使在线学习被限制在一个由富有表达力的基函数张成的子空间中。由于变换后问题简单，可采用标准粒子滤波器进行 Bayesian 推断。与大多数现有工作不同，所提方法能够在线学习以非线性方式嵌套在第一性原理模型内部的目标函数。此外，我们对因将学习限制于子空间而引入的误差给出了理论量化。一项使用非线性电池模型的 Monte-Carlo 仿真研究表明，与一个基线方法和一个最先进方法相比，所提方法能够以显著更少的粒子实现快速收敛。
英文摘要：
Intelligent real-world systems critically depend on expressive information about their system state and changing operation conditions, e.g., due to variation in temperature, location, wear, or aging. To provide this information, online inference and learning attempts to perform state estimation and (partial) system identification simultaneously. Current works combine tailored estimation schemes with flexible learning-based models but suffer from convergence problems and computational complexity due to many degrees of freedom in the inference problem (i.e., parameters to determine). To resolve these issues, we propose a procedure for data-driven offline conditioning of a highly flexible Gaussian Process (GP) formulation such that online learning is restricted to a subspace, spanned by expressive basis functions. Due to the simplicity of the transformed problem, a standard particle filter can be employed for Bayesian inference. In contrast to most existing works, the proposed method enables online learning of target functions that are nested nonlinearly inside a first-principles model. Moreover, we provide a theoretical quantification of the error, introduced by restricting learning to a subspace. A Monte-Carlo simulation study with a nonlinear battery model shows that the proposed approach enables rapid convergence with significantly fewer particles compared to a baseline and a state-of-the-art method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
