论文标题
非线性系统识别的神经普通微分方程
Neural Ordinary Differential Equations for Nonlinear System Identification
论文作者
论文摘要
神经普通微分方程（节点）最近被提出是非线性系统识别任务的有希望的方法。在这项工作中，我们将其预测性能与当前最新非线性和经典线性方法进行了比较。特别是，我们提出了一项定量研究，将节点的性能与神经状态空间模型和经典线性系统识别方法进行了比较。我们评估了每种方法在八个不同动态系统上的开环错误上的推理速度和预测性能。实验表明，与基准方法相比，节点可以通过数量级来始终如一地提高预测准确性。除了提高精度外，我们还观察到与神经状态空间模型相比，节点对超参数敏感。另一方面，这些性能提高会在推理时间略有增加计算。
Neural ordinary differential equations (NODE) have been recently proposed as a promising approach for nonlinear system identification tasks. In this work, we systematically compare their predictive performance with current state-of-the-art nonlinear and classical linear methods. In particular, we present a quantitative study comparing NODE's performance against neural state-space models and classical linear system identification methods. We evaluate the inference speed and prediction performance of each method on open-loop errors across eight different dynamical systems. The experiments show that NODEs can consistently improve the prediction accuracy by an order of magnitude compared to benchmark methods. Besides improved accuracy, we also observed that NODEs are less sensitive to hyperparameters compared to neural state-space models. On the other hand, these performance gains come with a slight increase of computation at the inference time.
