基于FMD-IBKA-BTGNN的船舶运动态度预测模型
Chunyuan Shi1, Yanguan Su2, Biao Zhang2
1School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, China.
这项研究引入了一种新的混合模型,用于在具有挑战性的海洋条件下预测船舶运动态度. FMD-IBKA-BTGN模型显著提高了预测准确性和概括能力.
科学领域:
- 海洋工程 海洋工程
- 海军建筑 海军建筑
- 海洋学 海洋学 海洋学
背景情况:
- 预测船舶运动态度对于海上安全和运营效率至关重要.
- 海洋环境带来了诸如非静止性和随机性等复杂的挑战,阻碍了准确的预测.
研究的目的:
- 开发一种先进的混合模型,用于准确预测船舶运动态度.
- 在动态的海洋条件下克服现有方法的局限性.
主要方法:
- 功能模式分解 (FMD) 用于信号分解.
- 改进了黑翼飞算法 (IBKA) 进行超参数优化.
- 双向时间卷积网络与门式循环单元 (BTGN) 进行预测.
- 集体学习将所有组件的预测结合起来.
主要成果:
- 拟议的FMD-IBKA-BTGN模型与其他六种模型相比,表现出了优越的性能.
- 预测错误的显著减少:20.38%的MAPE,7.4%的RMSE,4.2%的MAE和0.97%的MSE与LSTM相比.
- 该模型在Sea State 4条件下显示了增强的预测准确度和概括能力.
结论:
- 混合FMD-IBKA-BTGN模型为船舶运动态度预测提供了一个强大的解决方案.
- 病,IBKA和BTGN的整合有效地解决了海洋环境的复杂性.
- 这种方法提高了船舶运动预测的精度和可靠性.
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