人工神经双胞胎 - 在分布式过程链中优化过程和持续学习
Johannes Emmert1, Ronald Mendez1, Houman Mirzaalian Dastjerdi1
1Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, Division Development Center X-ray Technology, Flugplatzstr. 75, 90768 Fürth, Germany.
概括
本研究介绍了人工神经双胞胎,这是一个用于优化工业过程的新方法. 它通过整合人工智能,传感器网络和控制策略来提高经济和生态效率,以更好地管理数据和模型适应性.
科学领域:
- 工业过程工程 工业过程工程
- 人工智能的人工智能
- 控制系统 控制系统
背景情况:
- 整体的工业流程优化面临着由于数据主权,多样化的目标和专家知识要求的挑战.
- 在工业环境中,数据驱动的AI方法往往需要经常重新校准以解决分布偏移的问题.
- 目前的方法与分散的数据融合和可适应的流程控制扎.
研究的目的:
- 提出一种新的框架,即人工神经双胞胎,用于克服工业过程优化和控制的局限性.
- 为了使分散的,可差异化的数据融合在分布式过程步骤中进行状态估计.
- 通过梯度反向传播来促进过程优化和AI模型微调.
主要方法:
- 结合模型预测控制,深度学习和传感器网络的概念.
- 实施分散的,可差异化的数据融合,用于状态估计.
- 使用准神经网络结构来反向传播损失梯度.
- 在模拟的基于Unity的虚拟机场上进行示范,用于塑料回收.
主要成果:
- 人工神经双胞胎有效地集成分布式的过程步骤.
- 该方法允许基于过程参数和AI模型的梯度优化.
- 在模拟环境中成功演示突出了实际适用性.
- 在工业过程中提高经济和生态效率是可以实现的.
结论:
- 人工神经双胞胎为复杂的工业流程优化提供了强大的解决方案.
- 这一框架提高了适应分布式偏移的能力,并解决了数据主权问题.
- 该方法为更高效和可持续的工业运作提供了途径.
关键词:
持续的学习 持续的学习数据融合 - 数据融合分散和分散的控制是分散的和分散的.分布式学习是一种分布式学习.物联网的物联网,就是物联网.模型预测控制模型预测控制多传感器系统 多传感器系统过程优化 过程优化更多相关视频
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