Seaplane motion prediction based on EMD-CNN-GRU coupled modeling

Juan Gu; Fanliang Meng; Hui Li; Hongyu Li; Chen Deng

doi:10.1117/12.3051584

18 November 2024 Seaplane motion prediction based on EMD-CNN-GRU coupled modeling

Juan Gu, Fanliang Meng, Hui Li, Hongyu Li, Chen Deng

Author Affiliations +

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134030X (2024) https://doi.org/10.1117/12.3051584
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

The seaplane motion signal has strong nonlinearity, and it is difficult to make accurate predictions by traditional forecasting methods. For this reason, this paper proposes to use the combination of EMD and CNN-GRU to forecast two different motions of seaplane based on in-depth research on EMD and neural networks. Firstly, EMD is used to decompose the original non-smooth signal into smoother modal components, and then the CNN network, which has the strong ability to extract non-linear features, is combined with GRU, which has the function of long-term memory, and the CNN-GRU parallel neural network model is used to train each component, which improves the forecasting accuracy and reduces the complexity at the same time. Through comparison experiments with the CNN-GRU model, a single CNN model, and a single GRU model, the effectiveness of the proposed hybrid model in short-term forecasting is verified, which expands a new idea for the research of seaplane motion forecasting methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Juan Gu, Fanliang Meng, Hui Li, Hongyu Li, and Chen Deng "Seaplane motion prediction based on EMD-CNN-GRU coupled modeling", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134030X (18 November 2024); https://doi.org/10.1117/12.3051584

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