论文标题
使用神经网络的连续时间系统识别：模型结构和拟合标准
Continuous-time system identification with neural networks: Model structures and fitting criteria
论文作者
论文摘要
本文介绍了量身定制的神经模型结构和两个用于学习动力学系统的自定义拟合标准。提出的框架基于连续时间空间模型的系统行为的表示。为了最大程度地减少测量和估计输出之间的差异，隐藏状态的序列与神经网络参数一起进行了优化，并确保优化的状态序列与估计的系统动力学一致。通过三个案例研究证明了该方法的有效性，包括基于实验数据的两个公共系统识别基准。
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.
