基于人工神经网络和多项式回归的六组件力传感器解模型的研究
1School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China.
Sensors (Basel, Switzerland)
|May 11, 2024
概括
本研究引入了两阶段的人工神经网络模型,以解决混合负载条件下的六组分力传感器脱问题,在分类和回归阶段实现高精度.
科学领域:
- * 工程 * 工程师 *
- * 人工智能 * 人工智能
- * 传感器技术 * 传感器技术
背景情况:
- *多维混合负荷对六个组件的力传感器构成挑战.
- *解负载测量对于准确的传感器数据至关重要.
- * 现有的方法可能会在复杂的加载场景中扎.
研究的目的:
- *为六个组件的力传感器提出一种新的两级解模型.
- * 为了应对多维混合负载的挑战.
- * 提高力和矩测量的准确性和可靠性.
主要方法:
- * 开发一个双阶段的人工神经网络模型.
- *第一阶段:使用深度BP神经网络和63个负载类别标签进行六维负载分类.
- *第二阶段:六维负载回归,将多项式回归与BP神经网络相结合.
- * 采用纤维布拉格格 (FBG) 传感器的六组件力传感器的设计.
- *建立弹性体模拟和实验数据集以进行验证.
主要成果:
- * 模拟数据:在分类阶段准确率为93.65%.
- *模拟数据:在回归阶段,力为6.29%的平均绝对百分比误差 (MAPE) 和矩为3.24%的平均绝对百分比误差.
- *实验数据:在分类阶段准确率为87.80%.
- *实验数据:在回归阶段,力为5.63%,动量为4.82%.
结论:
- * 拟议的两阶段脱模型有效地解决了多维混合负载下六组件力传感器的负载脱问题.
- * 该模型在负载分类和回归阶段都表现出高精度,通过模拟和实验数据验证.
- *使用FBG传感器与开发的模型结合使用,有望提高传感器性能.
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