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.

翻译：查明非线性系统是一个具有挑战性的问题。在识别过程中,系统的实际知识可以用来通过限制从输入到输出的可能的绘图空间,大大改进预测性能。通常,物理模型包含从数据中必须学习的未知参数。典型方法通常限制可能的模型,或不得不采用引入偏差的模型近似值。按顺序排列的蒙特卡罗方法使学习能够避免对更一般的模型类别产生任何偏差。此外,还可以用来估计Bayesian环境中模型参数的后方分布。这一条为接连的蒙特卡洛提供了一般性介绍,并通过举例说明具体的算法来说明它如何自然地适合系统识别。方法在两个系统中加以说明:一个系统有两个分层水箱,可能溢出于罐中,另一个系统是疾病传播的分包模型。