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
Online-Zugang:	Volltext
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