预处理器
强化学习
概率逻辑
数据预处理
计算机科学
人工智能
机器学习
均方预测误差
钢筋
数据挖掘
工程类
结构工程
作者
Yunyu Wei,Zezong Chen,Chen Zhao,Xi Chen,Rui Yang,Jiangheng He,Chunyang Zhang,Sitao Wu
标识
DOI:10.1016/j.aei.2022.101806
摘要
• Propose a deterministic and probabilistic ship pitch forecasting system. • Develop a denoising-decomposition data preprocessing strategy. • Reinforcement learning are used to achieve multi-predictor integrated prediction. • Use the improved QRNN model for probabilistic prediction. The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.
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