Uncertainty-Informed Model Selection Method for Nonlinear System Identification and Interpretable Machine Learning

Citation

Gu Y & Wei H (2024) Uncertainty-Informed Model Selection Method for Nonlinear System Identification and Interpretable Machine Learning. In: Proceedings of 2024 32nd Mediterranean Conference on Control and Automation (MED). 2024 32nd Mediterranean Conference on Control and Automation (MED), Chania - Crete, Greece, 11.06.2024-14.06.2024. IEEE, pp. 909-914. https://doi.org/10.1109/med61351.2024.10566184

Abstract
Modeling uncertainty has been an active and important topic in the fields of data-driven modeling and machine learning. Uncertainty ubiquitously exists in any data modeling process, making it challenging to identify the optimal models among many potential candidates. This article proposes an uncertainty-informed method to address the model selection problem. The performance of the proposed method is evaluated on a dataset generated from a complex system model. The experimental results demonstrate the effectiveness of the proposed method and its superiority over conventional approaches. This method has minimal requirements for the length of training data and model types, making it applicable for various modeling frameworks.

Keywords
Data-driven modeling; Adaptation models; Uncertainty; Recurrent neural networks; Training data; Machine learning; Data models

• 2024Conference Uncertainty-Informed Model Selection Method For Nonlinear System Identification And Interpretable Machine Learning