This paper presents a transfer learning approach which enables fast and efficient adaptation of Recurrent Neural Network (RNN) models of dynamical systems. A nominal RNN model is first identified using available measurements. The system dynamics are then assumed to change, leading to an unacceptable degradation of the nominal model performance on the perturbed system. To cope with the mismatch, the model is augmented with an additive correction term trained on fresh data from the new dynamic regime. The correction term is learned through a Jacobian Feature Regression (JFR) method defined in terms of the features spanned by the model’s Jacobian with respect to its nominal parameters. A non-parametric view of the approach is also proposed, which extends recent work on Gaussian Process (GP) with Neural Tangent Kernel (NTK-GP) to the RNN case (RNTK-GP). This can be more efficient for very large networks or when only few data points are available. Implementation aspects for fast and efficient computation of the correction term, as well as the initial state estimation for the RNN model are described. Numerical examples show the effectiveness of the proposed methodology in presence of significant system variations.

本文提出了一种迁移学习方法，可以快速有效地适应动态系统的递归神经网络 (RNN) 模型。首先使用可用的测量值识别标称RNN模型。然后假设系统动力学发生变化，导致标称模型性能在受扰动的情况下出现不可接受的退化系统。为了应对这种不匹配，该模型增加了一个附加校正项，该校正项是根据来自新动态机制的新数据训练的。校正项是通过雅可比特征回归 (JFR) 方法学习的，该方法根据模型的雅可比行列相对于其标称参数跨越的特征定义。还提出了该方法的非参数视图，它将最近使用神经正切核 (NTK-GP) 的高斯过程 (GP) 的工作扩展到 RNN 案例 (RNTK-GP)。这对于非常大的网络或只有很少的数据点可用时会更有效。描述了快速有效计算校正项的实施方面，以及 RNN 模型的初始状态估计。