The input vector space optimization for LSTM deep learning model in real-time prediction of ship motions

Abstract

Vessel motions due to the ocean waves contribute to maritime operational safety and efficiency. Real-time prediction of deterministic ship motions in the coming future seconds is essential in decision-making when performing motions sensitive activities. The Long-Short Term Memory (LSTM) deep learning model provides a potential way for nonlinear ship motions prediction due to its capability in nonlinearity processing. Determination of a reasonable dimension of the input vector is critical in training the LSTM model. Conventionally, the optimal dimension for the input vector is selected by traversing an empirical preset range. Hence, it suffers both high computational cost and poor adaptation in determining the optimal input vector dimension. In the present work, an input vector space optimization method is proposed based on the dependence hidden in ship motion records of a sequence. Taking different correlation expressions into consideration, both the Impulse Response Function (IRF) based and Auto-correlation Function (ACF) based techniques are investigated for input vector space optimization. Numerical simulations are carried out for vilification and comparison purpose. The ACF technique is better in representing the auto-correlation hidden in the stochastic ship motions. And the ACF-based LSTM model performs better in both training efficiency and prediction accuracy.