Simulation Mooring Lines Tension Prediction Based onImproved Resnet-LSTM Model
ZHANG Huoming1,HUANG Min1,LU Pinglan2
1. Zhejiang Provincial Key Laboratory of Flow Measurement Technology, China Jiliang University, Hangzhou, Zhejiang 310018, China
2. Engineering Training Center, China Jiliang University, Hangzhou, Zhejiang 310018, China
Abstract The linear and nonlinear forces on an offshore floating platform mooring system in a complex and variable operating environment are predicted, and the number of hidden layers, iterations, and learning rate of the long-short cycle memory (LSTM) single-model prediction network are optimized during the prediction process. A hybrid prediction model with multilayer feature extraction characteristics of variable convolution and wavelet-based activation functions and variable threshold residual shrinkage prediction function is proposed to nonlinearly map the overall forces of the multi-point mooring system in response to the platform motion, analyze the model of multi-point mooring cables, and obtain the value of the mooring cable tension under the combined action of wind and wave currents. The mooring force simulation dataset is trained for prediction using LSTM single model, Resnet-LSTM hybrid model and improved hybrid model. The results show and the network parameters and prediction indicators are optimized, that the prediction accuracy can be as high as 0.9974 using the Resnet-LSTM hybrid model, and the improved Resnet-LSTM prediction using variable convolution is better than the unimproved model. It is demonstrated that the application of the improved hybrid prediction model based on Resnet-LSTM has the effect of improving the network performance in the application of nonlinear time-series feature prediction of tension in multipoint mooring systems.
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2024, Vol. 45 
