修改的非线性歇斯底里学方法用于触觉传感器
Gasak Abdul-Hussain1, William Holderbaum1, Theodoros Theodoridis1
1School of Science, Engineering and Environment, University of Salford, Salford M5 4WT, UK.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
这项研究使用反向传播 (BP) 神经网络来减少软触觉传感器中的歇斯底里错误. 这种新的方法显著提高了机器人和可穿戴技术应用中的传感器精度.
科学领域:
- 材料科学 材料科学 材料科学
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 软触觉传感器采用压电阻材料对于大面积传感应用至关重要.
- 这些传感器中的歇斯底里症显著影响了它们的操作准确性,这构成了一个重大挑战.
研究的目的:
- 开发和验证一种使用反向传播 (BP) 神经网络的新方法,以减轻导电纤维基触觉传感器中的歇斯底里非线性.
- 为了提高软触觉传感器的准确性和可靠性,用于先进的应用.
主要方法:
- 设计了基于导电纤维的四个传感器单元,以在不同的力序列下收集输出电阻数据.
- 训练了一个反向传播的神经网络,使用收集的数据来纠正阻力值并最大限度地减少预测错误.
- 进行了验证实验,以评估BP网络在减少歇斯底里错误方面的有效性.
主要成果:
- 反向传播网络培训显示出出色的融合,有效地调整参数以最大限度地减少预测错误.
- 提出的方法成功地将最大hysteresis误差从传感器的全尺度输出的24.2%降低到13.5%.
- 训练有素的BP网络准确地预测了传感器输出电阻,验证了该方法的有效性.
结论:
- 在BP神经网络的方法提供了一个有希望的解决方案,通过减轻hysteresis非线性来提高柔性触摸传感器的精度.
- 这一进步提高了触觉传感器的可靠性和效率,扩大了它们在软机器人,可穿戴技术,医疗设备和消费电子产品中的实用性.
- 虽然完全消除歇斯底里可能是无法实现的,但这种方法有效地修改了非线性,导致传感器输出精度更高.
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