线性变量差分传感器的非线性补偿使用先进的蛇优化集成与触角功能链接人工神经网络
Qiuxia Fan1, Xinqi Zhang1, Zhuang Wen1
1School of Automation and Software Engineering, Shanxi University, Taiyuan 030006, China.
Sensors (Basel, Switzerland)
|February 26, 2025
概括
本研究介绍了一种先进的蛇优化-触点功能链接人工神经网络 (ASO-TFLANN),以增强线性变量微分变压器 (LVDTs) 的线性范围. 这种新方法提高了测量精度,并扩大了振动测量应用的操作范围.
科学领域:
- 仪器仪表和测量仪器的使用
- 人工智能的人工智能
- 控制系统 控制系统
背景情况:
- 线性变量差分变压器 (LVDT) 对于测量振动和主动隔离至关重要.
- 增加位移的LVDT非线性限制了它们的测量范围.
- 现有的方法在训练过程中扎着过度适配和局部最佳值.
研究的目的:
- 为了扩大LVDT的线性测量范围.
- 开发用于LVDT校准的高级优化和神经网络模型.
- 为了提高LVDT测量的准确性和可靠性.
主要方法:
- 开发了一种增强的蛇优化 (SO) 算法,该算法结合了拉丁式超立方体采样和勒维飞行 (ASO).
- 一个电压位移测试台被用来在各种激发下收集LVDT数据.
- 通过使用收集的数据来训练ASO-TFLANN模型,以优化神经网络重量.
主要成果:
- ASO算法有效地解决了在梯度下降方法中常见的过拟合和局部最佳问题.
- 离线模拟和在线测试表明,LVDT的线性范围显著扩大.
- 提出的方法成功地减少了误差指标 (εfs) 并提高了整体测量准确性.
结论:
- ASO-TFLANN模型为扩大LVDT测量范围提供了一个强大的解决方案.
- 这种方法为提高LVDT测量精度和可靠性提供了可靠的基础.
- 这项研究验证了ASO算法在优化复杂系统方面的有效性.
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