空气弹振动隔离系统的对齐预测基于基因算法-反向传播神经网络的海洋轴
Song Liu1,2, Liang Shi1,2, Zhi-Wei Liu1,2
1Institute of Noise and Vibration, Naval University of Engineering, Wuhan 430033, China.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
在海洋系统中准确的轴轴对齐预测对于船舶安全至关重要. 一种新的遗传算法逆向传播 (GA-BP) 神经网络模型有效地使用操作数据预测了对齐状态,大大提高了对传统方法的准确性.
科学领域:
- 海洋工程 海洋工程
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 轴轴对齐对于船舶推进系统的安全性和稳定性至关重要.
- 空气弹振动隔离系统 (ASVIS) 有助于降低噪音和调整控制.
- 传统的对齐预测面临着诸如传感器故障和大型ASVIS模型复杂性等挑战.
研究的目的:
- 为ASVIS开发一个强大的对齐预测模型,克服传感器限制.
- 为了提高海洋推进系统的对齐预测准确性和可靠性.
- 引入一个数据驱动的方法来预测轴轴对齐状态.
主要方法:
- 使用ASVIS操作数据开发一个反向传播 (BP) 神经网络模型.
- 整合基因算法 (GA) 来优化BP网络权重和值,创建GA-BP模型.
- 在真实ASVIS上对GA-BP模型的实验验证.
主要成果:
- 该GA-BP模型准确地预测了对齐状态,平均平方误差 (MSE) 为0.0114.4.
- 与标准BP网络相比,GA-BP模型显示了显著的改善,减少了MSE约74%.
- 该模型成功地利用了操作数据,绕过了直接对齐测量传感器的需求.
结论:
- GA-BP神经网络提供了一种可靠和准确的方法来预测海洋轴轴对齐.
- 这种数据驱动的方法有效地解决了复杂海洋环境中传统方法的局限性.
- 开发的模型通过改进的对齐预测,提高了船舶推进系统的控制和稳定性.
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