论文标题
非线性状态空间模型的变分系统识别
Variational System Identification for Nonlinear State-Space Models
论文作者
论文摘要
本文考虑了非线性状态空间模型的参数估计，这是一个重要但具有挑战性的问题。我们通过采用差异推理（VI）方法来应对这一挑战，这是一种与最大似然估计有着深厚连接的原则性方法。这种VI方法最终将模型估算为优化问题的解决方案，该解决方案是确定性的，可拖动的，可以使用标准优化工具来解决。还详细介绍了具有加性高斯噪声的系统的这种方法的专业化。在数值上对所提出的方法进行了数值检查，这些方法涉及一系列的模拟和真实示例，重点介绍了参数初始化的鲁棒性。此外，对最新替代方案进行了有利的比较。
This paper considers parameter estimation for nonlinear state-space models, which is an important but challenging problem. We address this challenge by employing a variational inference (VI) approach, which is a principled method that has deep connections to maximum likelihood estimation. This VI approach ultimately provides estimates of the model as solutions to an optimisation problem, which is deterministic, tractable and can be solved using standard optimisation tools. A specialisation of this approach for systems with additive Gaussian noise is also detailed. The proposed method is examined numerically on a range of simulated and real examples focusing on the robustness to parameter initialisation; additionally, favourable comparisons are performed against state-of-the-art alternatives.
