Adaptive prediction for ship pitch motion using sliding data window and Lipschitz quotients method

XU Dongxing, YIN Jianchuan

doi:10.16411/j.cnki.issn1006-7736.2025.02.002

Journal of Dalian Maritime University >

2025 , Vol. 51 >Issue 2: 10 - 21

DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2025.02.002

Adaptive prediction for ship pitch motion using sliding data window and Lipschitz quotients method

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(1. Naval Architecture and Shipping College, Guangdong Ocean University, Zhanjiang 524005, China; 2. Guangdong Provincial Engineering Research Center for Ship Intelligence and Safety, Zhanjiang 524005, China; 3. Guangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching, Zhanjiang 524088, China)

Online published: 2025-01-02

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Abstract

In order to accurately reflect the nonlinear, stochastic, and non-stationary characteristics of ship pitch motion in real-time, an adaptive prediction model is proposed based on sliding data window and Lipschitz quotients method. Firstly, the sliding data window is employed as a local observer to segment the ship's pitch motion status data in real-time, and the Lipschitz quotients method is used to adaptively determine the order of the subsystems represented within the current sliding data window. Online small batch training samples are provided for the feed-forward neural network model by using sliding data window and Lipschitz quotients method, which can overcome the impact of single sample and big batch data samples on the performance of the neural network model. Then, to address the problem that feed-forward neural networks based on deterministic learning algorithms are prone to fall into local optimums, a feed-forward neural network model based on the improved butterfly optimization algorithm trainer is proposed to improve the prediction accuracy of the ship's pitch motion status. In the improved butterfly optimization algorithm, a mutation operator guided by the balancing factor and an information reorganization strategy with an optimal individual guidance mechanism are employed to enhance the algorithm's ability to avoid falling into a local optimum. Finally, the effectiveness and feasibility of the improved butterfly optimization algorithm and the adaptive prediction model are verified by using the benchmark test functions and the measured pitch motion status data from M.V. “YuKun”, respectively. The experimental results show that the improved butterfly optimization algorithm in respect of convergence speed and accuracy outranks the butterfly optimization algorithm, particle swarm optimization algorithm, and moth-flame optimization algorithm; The proposed adaptive prediction model has stronger generalization ability and higher prediction accuracy, and the average running time of each step is within 0.2s, which is less than the system sampling time of 1s. The proposed adaptive prediction model not only meets real-time requirements but also improves the accuracy of ship's pitch motion status prediction, which can provide a potential solution for online modeling of complex systems.

Key words： ship pitch motion prediction; sliding data window; Lipschitz quotients method; improved butterfly optimization algorithm; feed-forward neural network

Cite this article

XU Dongxing, YIN Jianchuan . Adaptive prediction for ship pitch motion using sliding data window and Lipschitz quotients method[J]. Journal of Dalian Maritime University, 2025 , 51(2) : 10 -21 . DOI: 10.16411/j.cnki.issn1006-7736.2025.02.002

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