基于改进RBF神经网络对船舶初稳性高度的非线性实时预报

    Nonlinear real-time prediction of ship’s metacentric height based on improved RBF neural network

    • 摘要: 针对船舶初稳性高度(GM)计算步骤繁杂和实时性差等问题,提出一种基于改进径向基函数神经网络(RBFNN)对船舶GM实时预报的方法。首先,引入leave-one-out交叉验证和早停策略优化径向基函数,提高模型的泛化性能;其次,选取琼州海峡客滚船“紫荆十一号”为研究对象,通过灰色关联分析方法选取4个与GM密切相关的因素作为神经网络的输入特征;最后,以经验公式计算所得的GM作为期待值,与不同算法得出的预报值进行对比分析。仿真试验结果表明,改进后的RBF神经网络比改进前具有更低的预报误差(IMSE为0.0004,IMAPE低于10%)。此外,与机器学习和其他人工神经网络对比,所提出的模型在船舶初稳性预报方面表现出更好的性能。因此,本文所提模型可作为船舶初稳性高度预报工具,为船舶智能配载提供准确实时的信息,提高运营效益。

       

      Abstract: Addressing the complexities and the lack of real-time capability in calculating the metacentric height(GM)of ships,a method for real-time prediction of a ship’s GM based on an improved Radial Basis Function Neural Network(RBFNN)is proposed. Firstly,leave-one-out cross-validation and early stopping strategies are introduced to optimize the RBF,enhancing the generalization performance of the model. Secondly,the passenger-cargo Ro-ro ship "Zijing 11" navigating in Qiongzhou Strait is selected as the research subject,and four factors closely related to GM are chosen as input features for the neural network through grey relational analysis. Finally,the GM calculated using an empirical formula is taken as the expected value and compared with the predicted values obtained by different algorithms for analysis. Simulation test results demonstrate that the improved RBFNN exhibits lower prediction errors compared to its predecessor(with MSE of 0.000425 and MAPE below 10%). Additionally,when compared to other machine learning algorithms and artificial neural networks,the proposed model exhibits superior performance in predicting the ship’s GM.Therefore,the model proposed in this paper can serve as a tool for predicting the GM of ships,providing accurate and real-time information for intelligent ship loading,and enhancing operational efficiency.

       

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