Accurate time-series forecasting of floating platform motion via a reinforced fusion CNN-BiLSTM-attention model.

ABSTRACT: Accurate motion prediction of floating platforms is critical for ensuring operational safety in offshore engineering applications or marine equipment testing. However, the strong nonlinearity and non-stationary characteristics induced by complex marine environments pose significant challenges to conventional prediction models. This study proposes a reinforced hybrid neural network (CNN-BiLSTM-Attention) integrated with advanced signal processing techniques to address these challenges. The methodology combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for multi-scale signal analysis, coupled with temporal feature engineering through sliding window optimization. And the architecture innovatively integrates convolutional neural networks for spatial pattern e

SUBMITTER: Zheng H 

PROVIDER: S-EPMC12863529 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature