Ship Motion Attitude Prediction Based on an Adaptive Dynamic Particle Swarm Optimization Algorithm and Bidirectional LSTM Neural Network

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 hyp...

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Veröffentlicht in:IEEE access Jg. 8; S. 90087 - 90098
Hauptverfasser: Zhang, Guoyin, Tan, Feng, Wu, Yanxia
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adaptive algorithms
ADPSO algorithm
Attitudes
BiLSTM neural network
Heuristic algorithms
Logic gates
Machine learning
Marine vehicles
Model accuracy
Neural networks
Optimization algorithms
Particle swarm optimization
prediction accuracy
Prediction algorithms
Prediction models
Predictions
Predictive models
Process parameters
Ship motion
Ship motion attitude
ISSN:2169-3536, 2169-3536
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2993909