A generalized grey model with symbolic regression algorithm and its application in predicting aircraft remaining useful life

As a sparse data analysis method, a grey model faces challenges in interpretability for its effective application in uncertain systems. This study proposes a generalized grey model (GGM) based on symbolic regression, designed to improve the intelligence and adaptability of grey models. The GGM serve...

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Vydáno v:Engineering applications of artificial intelligence Ročník 136; s. 108986
Hlavní autoři: Liu, Lianyi, Liu, Sifeng, Yang, Yingjie, Guo, Xiaojun, Sun, Jinghe
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.10.2024
Témata:
Forecasting algorithm
Grey system theory
Model interpretability
Structure identification
Time series
Structure identification
Model interpretability
Grey system theory
Forecasting algorithm
Time series
ISSN:0952-1976
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Shrnutí:As a sparse data analysis method, a grey model faces challenges in interpretability for its effective application in uncertain systems. This study proposes a generalized grey model (GGM) based on symbolic regression, designed to improve the intelligence and adaptability of grey models. The GGM serves as a unified framework, integrating various grey model families and addresses regression challenges to determine the model structure. Symbolic regression in the GGM identifies symbolic input-output relationships, offering an interpretable approach for structure determination. By leveraging the non-uniqueness principle in grey system theory and employing structural penalty parameters, the model balances complexity and interpretability. A comparative analysis between GGM and conventional grey function models is conducted focusing on the differences in modeling, structure identification, and parameter optimization. Validation on the M3 competition dataset demonstrated the GGM's superior performance, achieving a significant reduction in prediction error compared to other grey forecasting models. Additionally, a rigorous analysis of aircraft lifespan data underscored the robustness and accuracy of GGM in practical engineering applications. •An interpretable generalized grey model framework is constructed.•The symbolic regression algorithm is used to identify the structure of grey model.•The generalized grey model can strike a balance between complexity and interpretability.
ISSN:0952-1976
DOI:10.1016/j.engappai.2024.108986