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相关概念视频

State Space Representation01:27

State Space Representation

534
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
534

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相关实验视频

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一个新的适应软传感框架,用于标签延迟的工业数据流.

Lei Chen1, Guomin Wu1, Haoyan Dong1

  • 1Engineering Research Center of Digitized Textile & Fashion Technology, Ministry of Education, Donghua University, Shanghai 201620, China; College of Information Science and Technology, Donghua University, Shanghai 201620, China.

ISA transactions
|September 18, 2025
PubMed
概括

本研究介绍了适应软传感器对标签延迟 (ASSLD) 的应用,这是一种新的方法,可以提高适应软传感器的准确性,尽管数据延迟. ASSLD提高了在线适应性和数据利用,显著提高了性能.

关键词:
适应软传感器 适应软传感器数据流是数据流.标签延迟标签的时间延迟多级回归的多级回归方法在线更新在线更新.

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科学领域:

  • 化学工程是化学工程的重要组成部分.
  • 数据科学数据科学数据科学
  • 机器学习 机器学习

背景情况:

  • 工业数据流通常会延迟质量变量,阻碍自适应软传感器性能.
  • 及时更新对于在动态环境中准确的软传感器模型至关重要.

研究的目的:

  • 提出一种新的框架,即标签延迟适应软传感器 (ASSLD),以应对工业数据流中标签延迟的挑战.
  • 在数据采集延迟的情况下,提高软传感器的在线适应性和准确性.

主要方法:

  • 开发了一个自适应的多层回归模型,集成来自不同网络深度的输出.
  • 实施在线多元化数据库,通过样本选择和加权,有效地重用历史标记数据.
  • 在延迟期内使用未标记的样本,以适应最近的数据.

主要成果:

  • 拟议的ASSLD框架在处理标签延迟方面表现出有效性.
  • 与现有的基线方法相比,实现了超过12%的精度改进.
  • 在硫回收装置和聚数据集上验证.

结论:

  • ASSLD为具有延迟质量变量的软传感器应用提供了强大的解决方案.
  • 多层回归和多样化数据管理的整合提高了模型的适应性和预测准确性.
  • 这一框架为工业过程中的实时质量监测提供了重大进展.