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| Simulation Mooring Lines Tension Prediction Based on Improved Resnet-LSTM Model |
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Abstract To solve the tension prediction problem when the structure of the ocean floating platform mooring system was subjected to various linear and nonlinear forces in the complex and changeable operating environment, for improving the hidden layers, iteration times and learning rate parameters of the LSTM model based on long short term memory algorithm, the Resnet-LSTM model with convolution muti-layer features extraction inserted different thresholds’ residual module was established. The floating platform was regarded as an overall force loaded on the mooring lines to analysis nonlinear feature while influential factors of mooring lines tension were considered and the environment loads factors were calculated. After the muti-points mooring system was setup and solved under different ocean conditions, trained and predicted the lines tension data which came from datasets with three different models LSTM, Resnet-LSTM and improved Resnet-LSTM. Experiment comparative results showed that the Resnet-LSTM indicators of accuracy was up 0.9973, the parameters of network were optimized which proved the model based on improved Resnet-LSTM could effectively increase the prediction result of the model in nonlinear data processing of multi-points mooring lines system tension with some improvement value.
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Received: 11 April 2023
Published: 18 December 2024
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2024, Vol. 45 
