论文标题
非线性系统识别：在使用顺序蒙特卡洛法尊重物理模型的同时学习
Nonlinear System Identification: Learning while respecting physical models using a sequential Monte Carlo method
论文作者
论文摘要
识别非线性系统是一个具有挑战性的问题。可以在识别过程中使用对系统的物理知识，以通过限制从输入到输出的可能映射的空间来显着提高预测性能。通常，物理模型包含未知参数，必须从数据中学到。经典方法通常会限制可能的模型，或者必须诉诸引入偏见的模型的近似值。顺序的蒙特卡洛方法使学习能够在不引入更通用模型类别的情况下引入任何偏见。此外，它们还可以用来近似贝叶斯环境中模型参数的后验分布。本文提供了对顺序蒙特卡洛的一般介绍，并通过提供特定算法的示例来展示其自然适合系统识别的介绍。这些方法在两个系统上进行了说明：一个具有两个级联的水箱的系统，在两个储罐中可能溢出，并且是疾病扩散的隔室模型。
Identification of nonlinear systems is a challenging problem. Physical knowledge of the system can be used in the identification process to significantly improve the predictive performance by restricting the space of possible mappings from the input to the output. Typically, the physical models contain unknown parameters that must be learned from data. Classical methods often restrict the possible models or have to resort to approximations of the model that introduce biases. Sequential Monte Carlo methods enable learning without introducing any bias for a more general class of models. In addition, they can also be used to approximate a posterior distribution of the model parameters in a Bayesian setting. This article provides a general introduction to sequential Monte Carlo and shows how it naturally fits in system identification by giving examples of specific algorithms. The methods are illustrated on two systems: a system with two cascaded water tanks with possible overflow in both tanks and a compartmental model for the spreading of a disease.
