This paper presents tailor-made neural model structures and two custom fitting criteria for learning dynamical systems. The proposed framework is based on a representation of the system behavior in terms of continuous-time state-space models. The sequence of hidden states is optimized along with the neural network parameters in order to minimize the difference between measured and estimated outputs, and at the same time to guarantee that the optimized state sequence is consistent with the estimated system dynamics. The effectiveness of the approach is demonstrated through three case studies, including two public system identification benchmarks based on experimental data.

本文介绍了量身定制的神经模型结构和用于学习动力学系统的两个自定义拟合标准。所提出的框架基于以连续时间状态空间模型表示的系统行为。隐藏状态的序列与神经网络参数一起进行了优化，以最大程度地减少了实测输出与估算输出之间的差异，同时确保了优化状态序列与估算的系统动力学一致。通过三个案例研究证明了该方法的有效性，其中包括两个基于实验数据的公共系统识别基准。