This paper proposes a novel system identification scheme to obtain a MIMO model of ship maneuvering motion, which can leverage the temporal correlation from the constructed training data to learn the underlying feasible model robust to extraneous noise. The scheme is based on long–short-term-memory (LSTM) deep neural network, which is more easily trained than traditional feedforward neural network with more complicated network structure. First, multiple datasets of simulated standard maneuvers (10°/10° and 20°/20° zigzag, 35° turning circle) of a KVLCC2 model are artificially polluted with white noise of various levels and used simultaneously to train the deep neural network model. Meanwhile, the data of 15°/15° zigzag maneuver are used to facilitate the training process to alleviate overfitting problem. Second, different datasets of modified zigzag tests are used to validate the generalization performance and robustness to noise of the trained neural network model. The training and validation results demonstrate that a mapping between the dynamics of ship motion and the computation in LSTM deep neural network is correctly identified. This mapping indicates that the complex nonlinear features of ship maneuvering motion can be learned from the measured temporal data, using standard training techniques for deep neural networks. An equivalent LSTM deep neural network model with better generalization performance and robustness is established, and its accuracy in predicting ship maneuvering motion is validated.

本文提出了一种新颖的系统识别方案，以获取船舶操纵运动的MIMO模型，该模型可以利用所构造的训练数据中的时间相关性来学习对外部噪声具有鲁棒性的潜在可行模型。该方案基于长短期记忆（LSTM）深度神经网络，它比具有复杂网络结构的传统前馈神经网络更易于训练。首先，将KVLCC2模型的模拟标准操纵的多个数据集（10°/ 10°和20°/ 20°之字形，35°转弯圆）人工污染各种水平的白噪声，并同时用于训练深层神经网络模型。同时，使用15°/ 15°之字形操纵的数据来简化训练过程，以减轻过度拟合的问题。第二，修改后的之字形测试的不同数据集用于验证训练后的神经网络模型的泛化性能和对噪声的鲁棒性。训练和验证结果表明，可以正确识别船舶运动动力学与LSTM深层神经网络中的计算之间的映射。该映射表明，使用用于深度神经网络的标准训练技术，可以从所测得的时间数据中学习船舶操纵运动的复杂非线性特征。建立了具有较好泛化性能和鲁棒性的等效LSTM深层神经网络模型，并验证了其在预测船舶操纵运动中的准确性。训练和验证结果表明，可以正确识别船舶运动动力学与LSTM深层神经网络中的计算之间的映射。该映射表明，使用用于深度神经网络的标准训练技术，可以从所测得的时间数据中学习船舶操纵运动的复杂非线性特征。建立了具有较好泛化性能和鲁棒性的等效LSTM深层神经网络模型，并验证了其在预测船舶操纵运动中的准确性。训练和验证结果表明，可以正确识别船舶运动动力学与LSTM深层神经网络中的计算之间的映射。该映射表明，使用用于深度神经网络的标准训练技术，可以从所测得的时间数据中学习船舶操纵运动的复杂非线性特征。建立了具有较好泛化性能和鲁棒性的等效LSTM深层神经网络模型，并验证了其在预测船舶操纵运动中的准确性。使用针对深度神经网络的标准训练技术。建立了具有较好泛化性能和鲁棒性的等效LSTM深层神经网络模型，并验证了其在预测船舶操纵运动中的准确性。使用针对深度神经网络的标准训练技术。建立了具有较好泛化性能和鲁棒性的等效LSTM深层神经网络模型，并验证了其在预测船舶操纵运动中的准确性。