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In this paper, a fast prediction model was established for ship motion and load based on Gated Recurrent Neural Networks (GRU). GRU neural network is a concise and efficient recurrent neural network that captures the temporal information of training samples to establish a model for predicting unknown samples. The forecast model consisted of two independent GRU neural networks used to predict ship motion and load respectively. The historical ship pitch and heave data were jointly used as the input of the motion prediction model to predict the ship pitch and heave in the next few seconds. The motion prediction results were used as the input of the load prediction model to achieve the prediction of the vertical bending moment in the midship. The method was validated through model test data, and the results showed that the prediction results at different lead times were in good agreement with the test results in terms of amplitude and phase, verifying the feasibility of the established ship motion and load prediction model.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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