Heave motion of ships is a complex nonlinear dynamic process and cannot be accurately forecasted using a single prediction model. In this paper, an effective combined forecasting method is proposed to perform ship's heave motion prediction. The proposed method combines back propagation neural network (BPNN), autoregressive model (AR) and extreme learning machine (ELM) through an induced ordered weighted averaging (IOWA) operator. The prediction accuracy is selected as the induced variable and the prediction results are sorted according to prediction accuracy and IOWA operator assigns larger weights to the position with the smallest prediction error. The optimal weights are determined by maximizing the B-mode relational degree. Experimental results demonstrate its effectiveness of the proposed method. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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基于诱导有序加权平均算子的船舶垂荡联合预报

船舶的垂荡运动是一个复杂的非线性动力过程，无法用单一的预测模型对其进行准确预测。在本文中，提出了一种有效的组合预测方法来进行船舶的升沉运动预测。所提出的方法通过诱导有序加权平均 (IOWA) 算子将反向传播神经网络 (BPNN)、自回归模型 (AR) 和极限学习机 (ELM) 相结合。选择预测精度作为诱导变量，根据预测精度对预测结果进行排序，IOWA算子对预测误差最小的位置赋予较大的权重。通过最大化 B 模式关联度来确定最佳权重。实验结果证明了该方法的有效性。© 2022 日本电气工程师协会。由 Wiley Periodicals LLC 出版。