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从热气动态参数传感器的神经网络信号集成,用于在飞行操作条件下的直升机轮轴发动机
Serhii Vladov1, Lukasz Scislo2, Valerii Sokurenko3
1Department of Scientific Work Organization and Gender Issues, Kremenchuk Flight College of Kharkiv National University of Internal Affairs, 17/6 Peremohy Street, 39605 Kremenchuk, Ukraine.
一种新的神经网络方法通过整合热气动态参数来提高直升机轮轴发动机传感器的精度到99.5%. 这种先进的过技术显著减少了错误,提高了发动机可靠性和飞行安全.
科学领域:
- 航空航天工程 航空航天工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 直升机轮轴发动机需要精确的热气动态参数监测,以确保安全高效的运行.
- 现有的传感器数据经常存在不准确性,需要强大的实时校正方法.
研究的目的:
- 开发和应用一种神经网络方法,用于整合直升机轮轴发动机热气动态参数.
- 通过实时数据校正来提高传感器读数的准确性和可靠性.
主要方法:
- 使用过方法开发一个神经网络,以闭环整合发动机参数.
- 基于反向传播错误和自适应性学习速率的神经网络训练算法的实施.
- 拟议方法与传统过器 (中位数-递归,递归,中位数) 的数学比较.
主要成果:
- 在280个训练时代后,实现了99.5%的准确性和0.5%的损失函数.
- 证明了显著的错误减少:2.11x与中位数递归相比,2.89x与递归相比,以及4.18x与中位数过器相比.
- 通过控制过拟合和考虑错误动态,验证了模型的概括能力.
结论:
- 神经网络方法显著提高了直升机轮轴发动机传感器读数的准确性和可靠性.
- 这种进步提高了飞机的运营效率和飞行安全.
- 开发的技术通过先进的数据处理为航空业提供了新的前景.
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