同时选模型结构和参数，让RNN更精准且抗噪声。
A new approach for combined model class selection and parameters learning for auto-regressive neural models
- 用集合成员法联合优化模型类型与参数。
- 在有界噪声下仍保持高精度，适合控制场景。
- 解决自回归模型训练中的难题，适合工业应用。
本文提出一种针对非线性动态系统辨识的新型联合模型结构选择与参数学习方法。聚焦于一类特定的循环神经网络——带外生输入的非线性自回归回声状态网络（NARXESNs），该方法通过新的集成员法（set-membership, SM）实现模型类别的同时选择与参数学习。实验表明，该方法能识别出简洁且精确的模型，适用于控制任务。此外，所提框架提供了一种鲁棒训练策略，显式考虑有界测量噪声，并在参数学习过程中允许基于数据一致性的仿真性能评估，而这一过程对于具有自回归成分的模型通常属于NP难问题。
原文摘要 · Abstract (English)
This work introduces a novel approach for the joint selection of model structure and parameter learning for nonlinear dynamical systems identification. Focusing on a specific Recurrent Neural Networks (RNNs) family, i.e., Nonlinear Auto-Regressive with eXogenous inputs Echo State Networks (NARXESNs), the method allows to simultaneously select the optimal model class and learn model parameters from data through a new set-membership (SM) based procedure. The results show the effectiveness of the approach in identifying parsimonious yet accurate models suitable for control applications. Moreover, the proposed framework enables a robust training strategy that explicitly accounts for bounded measurement noise and enhances model robustness by allowing data-consistent evaluation of simulation performance during parameter learning, a process generally NP-hard for models with autoregressive components.
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