Vessel Track Prediction Based on Fractional Gradient Recurrent Neural Network with Maneuvering Behavior Identification

Han, Xue; Chai, Tian You

doi:10.1155/2021/5526082

Cited by 4 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During the training, each language coder was prepared step-by-step, followed by two language coders. Finally, the key for each link is to follow the link [18,19].…”

Section: Model Training Frameworkmentioning

confidence: 99%

Design of English Translation Mobile Information System Based on Recurrent Neural Network

Gao¹

2022

Mobile Information Systems

View full text Add to dashboard Cite

To solve the problem of translating lines of difference in length into English, this article presents a model of neural network recovery (RNN) English translator-based models of end-to-end encoder-decoder. This method promotes machine autonomous learning of features and transforms corpus data into word vectors by constructing end-to-end. By mapping the source language and target language directly through the recurrent neural network and selecting semantic error to construct objective function during training, the influence of each part in semantic can be well balanced, and the alignment information is fully considered, which provides powerful guidance for deep recurrent neural network training. The results of the neural network test define the standard BLEU score by 1.51–11.86. Our test scores and BLEU scores at all levels show that data in equivalence play an important role in modeling. Summary. the English translation model based on the neural repetitive fusion is efficient and stable.

show abstract

“…During the training, each language coder was prepared step-by-step, followed by two language coders. Finally, the key for each link is to follow the link [18,19].…”

Section: Model Training Frameworkmentioning

confidence: 99%

Design of English Translation Mobile Information System Based on Recurrent Neural Network

Gao¹

2022

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…Each input value of RNN only establishes a weighted connection with its own route. e error signal is defined as [18] e(t) � o(t) − y(t).…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Bridge-Ship Collision Avoidance Control Based on AFSMC with a FRNN Estimator

Fang

Cao²

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

For collision avoidance and maneuvering control in bridge areas, an adaptive fractional sliding mode control with fractional recurrent neural network (FRNN-AFSMC) is proposed. The uncertainties are estimated by FRNN, and the fractional gradient is adopted to improve the recurrent neural network (RNN). Its convergence has been proven. The influence of fractional order on algorithm performance is analyzed, and the simulation platform of ship collision avoidance control is built. Dynamic collision avoidance of multiple ships is simulated and verified. The results show the feasibility and effectiveness of dynamic autonomous collision avoidance motion control in a dynamic ocean environment.

show abstract

Charting the Course of Ship Track Prediction: A Novel Approach for Maritime Traffic Analysis and Enhanced Situational Awareness

Eljabu,

Etemad,

Matwin

2023

2023 IEEE International Conference on Big Data (BigData)

View full text Add to dashboard Cite

Vessel Track Prediction Based on Fractional Gradient Recurrent Neural Network with Maneuvering Behavior Identification

Cited by 4 publications

References 17 publications

Design of English Translation Mobile Information System Based on Recurrent Neural Network

Design of English Translation Mobile Information System Based on Recurrent Neural Network

Bridge-Ship Collision Avoidance Control Based on AFSMC with a FRNN Estimator

Charting the Course of Ship Track Prediction: A Novel Approach for Maritime Traffic Analysis and Enhanced Situational Awareness

Contact Info

Product

Resources

About