A catenary single-point mooring requires a simulation environment based on inputs such as basic conditions, operating conditions, self-storage conditions, motion and force requirements, and multi-point testing to find the optimal design. The paper uses deep learning method to solve the mooring system prediction problem. Firstly, two datasets, i.e., self dataset and operation dataset are acquired by simulation calculation. Then the self dataset is predicted and the data is divided into three categories: local, global, and global plus local for training and validation, and a two-layer full-connected neural network is used to predict the regression problem with an accuracy of over 90%. As the results are not satisfactory when the model is applied to more complex operation datasets, a self-built model DBRNet12 complex network using DNN+BN+ReLU as the minimum component is added to handle more operation data, thus obtaining an average accuracy of 86%. The self-built RNet40 network based on the idea of residuals on DBRNet12 achieves a 90% average accuracy. In terms of network architecture, a deep neural network is built to predict parameters through fully connected layers, and the network structure is continuously optimized. Finally, the evaluation of relative error is used to evaluate the effectiveness of the prediction and the residual network is used for optimization. Through this procedure, the application effect of deep learning methods in mooring system prediction problems is achieved, and the ideas provide references for further research and practice in this field.

multiple regression  /  single point mooring  /  deep learning  /  residual network