新型的深度学习解决方案与层次的反复神经网络为非线性刚性达尔歇斯底里模型在压电执行器
Aneela Kausar1, Chuan-Yu Chang1, Sidra Naz2
1Graduate school of Engineering Science and Technology, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan.
概括
这项研究使用人工智能和神经计算来模拟压电执行器 (PEAs) 的非线性歇斯底里. 该方法通过准确预测执行器行为来增强PEA精确定位,克服现有方法的局限性.
科学领域:
- 工程 工程师 工程师 工程师
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 压电执行器 (PEAs) 对于先进制造中的精确定位至关重要.
- 非线性歇斯底里效应显著降低PEA性能.
- 现有的歇斯底里模拟技术要么复杂,对变化敏感,要么需要大量数据,缺乏稳定性.
研究的目的:
- 开发一种可靠,准确的方法来建模PEAs中的非线性歇斯底里.
- 为了克服传统歇斯底里补偿技术的局限性.
- 通过改进的建模,增强PEA的精确定位能力.
主要方法:
- 利用人工智能,特别是神经计算,以莱文伯格-马奎特 (LM) 优化进行前和反向传播网络.
- 分析了达尔歇斯底里斯模型 (DHM) 使用合成生成的数据从数值集成的DHM方程.
- 从各种激发输入和参数变化产生的位移时间序列上训练有素的循环神经网络 (RNN).
主要成果:
- 通过使用反向传播循环神经网络 (BP-RNNs) 和LM优化成功近似解决方案和优化值.
- 在对PEAs的DHM建模中证明了AI方法的有效性.
- 使用诸如平均平方误差 (MSE),直方形分析和回归分析等指标验证了模型的准确性.
结论:
- 开发的人工智能驱动方法为建模PEA歇斯底里提供了稳定和可解释的解决方案.
- 这项研究提高了对PEA行为的理解和可预测性.
- 这些发现为使用PEAs改进高性能精确定位应用铺平了道路.
相关概念视频
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