Abstract
A new neural network prediction model is proposed for predicting ship motion attitude with high accuracy. This prediction model is based on an adaptive dynamic particle swarm optimization algorithm (ADPSO) and bidirectional long short-term memory (BiLSTM) neural network, which is to optimize the hyperparameters of BiLSTM neural network by the proposed ADPSO algorithm. The ADPSO algorithm introduces dynamic search space strategy into the classical particle swarm optimization algorithm and adjusts the learning factor adaptively to balance the global and local search ability, so as to improve the optimization performance and improve its optimization effect in BiLSTM parameter optimization process. The results show that the model can obtain higher prediction accuracy and faster convergence speed, and has better prediction performance in the prediction of ship motion attitude.
Highlights
Under the influence of various external factors such as bad weather, ships sailing on the sea are easy to produce six degrees of freedom random and complicated motions, including roll, pitch, yaw, sway, surge and heave
This paper proposes an improved adaptive dynamic particle swarm optimization algorithm (ADPSO), which is improved on the basis of PSO algorithm and can dynamically adjust the parameters of the algorithm to adjust the position of particles, so as to ensure that particles can find the global optimal solution
The results show that the ADPSO-bidirectional long short-term memory (BiLSTM) neural network model reflects better ship motion prediction performance in ship motion attitude prediction
Summary
Under the influence of various external factors such as bad weather, ships sailing on the sea are easy to produce six degrees of freedom random and complicated motions, including roll, pitch, yaw, sway, surge and heave. These movements have a great impact on the safety of ships and its personnel, the efficiency and safety of maritime operations, and especially on the take-off and landing of carrier-borne aircraft on aircraft carriers [1]. Professor Shen first introduced the theory of gray system into the prediction of ship swaying motion, the experimental results show that the model can basically describe the development trend of real data, but sometimes the error
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