用机器学习设计的人工神经网络为多相编码器设计
Sergio Alvarez-Rodríguez1, Francisco G Peña-Lecona1
1Laboratorio de Fotónica y Materiales, CU-Lagos (Centro Universitario de los Lagos), Universidad de Guadalajara, Lagos de Moreno 47460, Mexico.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
这项研究引入了使用人工神经网络的神经编码器,以精确地从光极化数据中确定角度位置. 该系统实现了高精度,即使在杂的工业环境中.
科学领域:
- 光电学是指光电子产品.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 管理复杂的数据需要先进的处理技术.
- 光学编码器对于测量角度位置至关重要.
- 人工神经网络 (ANN) 提供了强大的数据解释能力.
研究的目的:
- 设计和实施一个反向传播的多层人工神经网络 (ANN),用于处理光学编码器数据.
- 开发一种机器学习技术,用于训练ANN来预测角度位置.
- 为了提高工业传感器的角度测量的精度和范围.
主要方法:
- 使用反向传播的多层ANN,具有矢量输入,隐藏层和输出节点.
- 采用光的相位转移装置从光学编码器作为输入.
- 开发了一种培训方法,以确保0360°测量范围的准确性.
- 在模拟环境中使用总错误作为主要指标来评估性能.
主要成果:
- 神经编码器在预测角度位置方面表现出了显著的准确性.
- 提出的方法成功地将高性能扩展到更大的角度间隔 (0360°).
- 模拟证实了系统的有效性,以总错误为关键绩效指标.
结论:
- 反向传播的ANN对于解释光学编码器数据以实现精确的角度传感是有效的.
- 开发的神经编码系统为高精度角测量提供了可行的解决方案.
- 这项技术可以在具有挑战性的工业环境中显著提高分辨器和其他多相传感器的性能.
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