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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| Published in: | IEEE access Vol. 8; pp. 90087 - 90098 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Piscataway
IEEE
2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2169-3536, 2169-3536 |
| Online Access: | Get full text |
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| 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. |
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| AbstractList | 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. |
| Author | Tan, Feng Wu, Yanxia Zhang, Guoyin |
| Author_xml | – sequence: 1 givenname: Guoyin surname: Zhang fullname: Zhang, Guoyin organization: College of Computer Science and Technology, Harbin Engineering University, Harbin, China – sequence: 2 givenname: Feng orcidid: 0000-0002-5278-9539 surname: Tan fullname: Tan, Feng organization: College of Computer Science and Technology, Harbin Engineering University, Harbin, China – sequence: 3 givenname: Yanxia surname: Wu fullname: Wu, Yanxia email: wuyanxia@hrbeu.edu.cn organization: College of Computer Science and Technology, Harbin Engineering University, Harbin, China |
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| SubjectTerms | 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 |
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| Title | Ship Motion Attitude Prediction Based on an Adaptive Dynamic Particle Swarm Optimization Algorithm and Bidirectional LSTM Neural Network |
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