In recent years, several algorithms for system identification with neural state-space models have been introduced. Most of the proposed approaches are aimed at reducing the computational complexity of the learning problem, by splitting the optimization over short sub-sequences extracted from a longer training dataset. Different sequences are then processed simultaneously within a minibatch, taking advantage of modern parallel hardware for deep learning. An issue arising in these methods is the need to assign an initial state for each of the sub-sequences, which is required to run simulations and thus to evaluate the fitting loss. In this paper, we provide insights for calibration of neural state-space training algorithms based on extensive experimentation and analyses performed on two recognized system identification benchmarks. Particular focus is given to the choice and the role of the initial state estimation. We demonstrate that advanced initial state estimation techniques are really required to achieve high performance on certain classes of dynamical systems, while for asymptotically stable ones basic procedures such as zero or random initialization already yield competitive performance.

本文对神经状态空间模型的系统识别算法的学习问题进行了深入的研究，探讨了初始状态估计的选择和作用，并在两个系统识别基准测试上进行了大量实验和分析，表明高级初始状态估计技术对于某些类型的动态系统需要实现高性能，而对于渐近稳定的系统，则基本过程，如零或随机初始化，已经具有竞争性能。

学习神经状态空间模型：是否需要状态估计器？