在物联网环境中的智能网络物理系统上嵌入式多层感知器人工神经网络的性能分析
Mayra A Torres-Hernández1,2,3, Miguel H Escobedo-Barajas2,3, Héctor A Guerrero-Osuna2
1Instituto Politécnico Nacional, Unidad Profesional Interdisciplinaria de Ingeniería Campus Zacatecas, Calle Circuito Cerro del Gato No. 202, Col. Ciudad Administrativa, Zacatecas 98160, Mexico.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
这项研究比较了人工智能 (AI) 软件在个人电脑,云计算和智能网络物理系统中进行字符识别. 这三种技术都实现了97%的准确性,这表明雾中人工智能技术是智能系统的可行选择.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络物理系统 网络物理系统
背景情况:
- 现代社会正在经历着巨大的转型,由数字化和物联网,云计算和机器学习等技术推动.
- 这些进步正在实现智能工厂和工业4.0的概念.
- 智能网络物理系统中的智能主要是基于软件的,这使得AI软件设计成为一个关键的研究领域.
研究的目的:
- 为了研究和比较一个多层感知的人工神经网络的性能,用于识别字符.
- 通过三个不同的实施技术来评估这个AI模型:个人计算机,云计算环境和智能网络物理系统.
主要方法:
- 开发和实施多层感知人造神经网络用于字符识别.
- 在个人电脑,云计算平台和智能网络物理系统上训练和测试人工神经网络.
- 基于培训时间和准确度指标的绩效评估.
主要成果:
- 在所有三种测试技术中,多层感知器实现了97%的类似精度.
- 在每个实施环境所需的培训时间中观察到显著的差异.
- 嵌入雾技术的人工智能展示了智能网络物理系统的有希望的性能.
结论:
- 该研究验证了多层感知子网络在工业4.0应用中的字符识别的有效性.
- 虽然准确性是一致的,但实施技术显著影响AI模型培训时间.
- 基于雾的人工智能为开发先进的智能网络物理系统提供了引人注目的高效替代方案.
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