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基于深度学习的多响应优化方法,用于双轴MEMS加速器.

Fahad A Mattoo1,2, Tahir Nawaz1,2, Muhammad Mubasher Saleem1,2

  • 1Department of Mechatronics Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.

Micromachines
|July 8, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种深度神经网络 (DNN),用于优化双轴微电子机械系统 (MEMS) 加速计. DNN模型有效地分析设计参数并优化多个传感器响应,优于现有方法.

关键词:
深度学习 (DL) 是指深度学习.深度神经网络是一个神经网络.双轴MEMS加速度计 双轴MEMS加速度计微电子机械系统 (MEMS) 是指微电子机械系统.多响应优化多响应优化神经网络的神经网络的神经网络

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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
  • 计算机科学 计算机科学

背景情况:

  • 微电子机械系统 (MEMS) 电容加速度计对于惯性传感至关重要.
  • 优化MEMS加速度计设计需要在几何参数和操作条件之间进行复杂的权衡.
  • 现有的优化方法可能是计算密集型,可能无法有效地处理多响应目标.

研究的目的:

  • 开发一种基于深度神经网络 (DNN) 的新方法来优化双轴MEMS电容加速度计的设计.
  • 创建一个统一的模型,分析个别设计参数和操作条件对传感器性能的影响.
  • 为了实现MEMS加速度计多个输出响应的高效同时优化.

主要方法:

  • 一个深度神经网络 (DNN) 模型被设计和训练,使用几何设计参数和操作条件作为输入.
  • 用DNN模型预测MEMS加速度计的输出响应.
  • 基于DNN的优化性能与基于计算机实验设计 (DACE) 的多响应优化方法进行了比较.

主要成果:

  • 基于DNN的模型有效地分析了个别设计参数对MEMS加速度计输出响应的影响.
  • 拟议的DNN方法有效地实现了多个输出响应的同时优化.
  • 与DACE相比,DNN方法表现出更高的性能,显示较低的平均绝对误差 (MAE) 和根平均平方误差 (RMSE).

结论:

  • 深度神经网络为双轴MEMS电容加速度计的设计优化提供了一种高效有效的方法.
  • 基于DNN的方法提供了一个强大的工具来分析参数灵敏度和优化复杂的MEMS设备.
  • 这项工作为MEMS加速度计优化建立了新的基准,突出了DNN在微型设备设计中的潜力.