Response predictions using the observed autocorrelation function

Nielsen, Ulrik Dam; Brodtkorb, Astrid H.; Jensen, Jørgen Juncher

doi:10.1016/j.marstruc.2017.10.012

Cited by 23 publications

(21 citation statements)

References 23 publications

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“…The approach to ship motion prediction taken by Nielsen et al also relies on density functions of time series data [10]. By deriving the observed autocorrelation matrix for variables largely dictated by the induced wave force, predictions of 15-60 seconds were made on a model-scale ship.…”

Section: A Model-based Motion Predictionmentioning

confidence: 99%

A Hybrid Approach to Motion Prediction for Ship Docking—Integration of a Neural Network Model Into the Ship Dynamic Model

Skulstad

Fossen

et al. 2021

IEEE Trans. Instrum. Meas.

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While automatic controllers are frequently used during transit operations and low-speed maneuvering of ships, ship operators typically perform docking maneuvers. This task is more or less challenging depending on factors such as local environment disturbances, number of nearby vessels, and the speed of the ship as it docks. This paper proposes a tool for onboard support that offers position predictions based on an integration of a supervised machine learning (ML) model of the ship into the ship dynamic model. The ML model is applied as a compensator of the unmodelled behaviour or inaccuracies from the dynamic model. The dynamic model increases the amount of predetermined knowledge about how the vessel is likely to move and thus reduces the black-box factor typically experienced in purely data-driven predictors. A prediction horizon of 30 seconds ahead of real time during docking operations is examined. History data from the 29meter coastal displacement ship RV (Research Vessel) Gunnerus is applied to validate the approach. Results show that the inclusion of the data-based ML model significantly improves the prediction accuracy.

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Section: A Model-based Motion Predictionmentioning

confidence: 99%

A Hybrid Approach to Motion Prediction for Ship Docking—Integration of a Neural Network Model Into the Ship Dynamic Model

Skulstad

Fossen

et al. 2021

IEEE Trans. Instrum. Meas.

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“…It can obtain better prediction results through the establishment of a simple mathematical model without large datum. Traditional methods have the physics-based numerical methods and some data-driven methods like classical time series models, which have poor prediction effects with complex model [16].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Prediction in Vessel Heave Motion Based on Improved LSTM Model

et al. 2021

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In order to solve the problem of control performance degradation caused by time delay in wave compensation control system, predicting vessel heave motion can be the input vector of the control system to alleviate time delay problem. The vessel heave motion belongs to the problem of time series, this paper proposes an improved Long Short-Term Memory (LSTM) model with a random deactivation layer (dropout), which can deal with the time series problem very well. In order to obtain the vessel heave motion, this paper establishes a wave model suitable for marine operation, and solves the vessel heave motion through the mathematical model of vessel motion. Finally, the paper predicts the vessel heave motion in a short predicted time series. In the process of obtaining the prediction effect of vessel heave motion, the Back Propagation (BP) neural network and the standard LSTM neural network are used to compare with the improved LSTM neural network. While the predicted time series is 0.1 s at sea state 3, the mean absolute percentage (MAPE) errors of BP neural network in the prediction of vessel heave motion is 1.06 × 10 −2 %, the standard LSTM in the prediction of heave motion is 1.43×10 −4 %, the improved LSTM in the prediction of heave motion is 7.51 × 10 −6 %. The improved LSTM improves MAPE by 1.05 × 10 −2 % compared with the BP and 1.42 × 10 −4 % compared with the standard LSTM. The prediction results show that the improved LSTM has a strong prediction capability with not easily overfitted in vessel heave motion prediction. The results show that the improved LSTM provides a new idea for vessel motion prediction and solves the problem of time delay, which is useful for the study of stability in marine operations.INDEX TERMS Short-term prediction, improved long short-term memory (LSTM), vessel heave motion, time delay.

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“…In addition, the training samples of the gray prediction method cannot be too many, so the gray prediction method has limitations in nonlinear time series prediction such as ship motion sequence. The Kalman filtering method has relatively mature theories, but it is more suitable for linear systems and requires accurate mathematical modeling of ship dynamics [4]. When the marine environment changes, the prediction accuracy will be greatly reduced.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Tan

2020

IEEE Access

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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. INDEX TERMS Ship motion attitude, BiLSTM neural network, ADPSO algorithm, prediction accuracy.

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Response predictions using the observed autocorrelation function

Cited by 23 publications

References 23 publications

A Hybrid Approach to Motion Prediction for Ship Docking—Integration of a Neural Network Model Into the Ship Dynamic Model

A Hybrid Approach to Motion Prediction for Ship Docking—Integration of a Neural Network Model Into the Ship Dynamic Model

Short-Term Prediction in Vessel Heave Motion Based on Improved LSTM Model

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

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