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相关概念视频

State Space Representation01:27

State Space Representation

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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...
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Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

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Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
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相关实验视频

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A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents
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基于多个过程状态的转移学习和集合学习的自适应软传感器.

Nobuhito Yamada1, Hiromasa Kaneko1

  • 1Department of Applied Chemistry School of Science and Technology Meiji University Kawasaki Japan.

Analytical science advances
|May 8, 2024
PubMed
概括

本研究引入了一种适应性软件传感器,用于预测焚烧厂的过程变量. 该技术使用转移学习,准确地预测产品质量在多个等级,甚至是新的.

科学领域:

  • 化学工程是化学工程的重要组成部分.
  • 机器学习 机器学习
  • 过程控制 过程控制

背景情况:

  • 准确预测工艺变量对于优化工厂运营和产品质量至关重要.
  • 传统的软传感器经常在不同的操作条件和新产品等级的引入中扎.
  • 转移学习提供了一种有前途的方法,可以在不同的数据集或操作环境中适应模型.

研究的目的:

  • 开发一种可适应的软件传感器技术,用于预测目标等级的客观过程变量.
  • 利用来自其他等级 (源域) 的数据来提高使用转移学习的目标等级的预测.
  • 为了自动管理源域选择,以防止负转移和增强模型的稳定性.

主要方法:

  • 利用转移学习,将目标等级数据集定义为目标域,其他等级定义为源域.
  • 通过改变每个源域的样本数量来构建多个子模型.
  • 采用局部加权部分最小平方 (LW-PLS) 方法用于自适应软传感器子模型构建.
  • 通过结合多个子模型的预测来预测客观变量值来实现集体学习.
  • 整合了一个自动源域判断机制,以减轻负面转移.

主要成果:

  • 拟议的自适应软件传感器技术证明了在实际的焚烧厂中准确预测产品质量.
关键词:
适应式软传感器 适应式软传感器组合学习组合学习局部加权的部分最小平方.多个等级的多个等级.负转移转移是一个负转移.转移学习转移学习

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  • 这种方法证明有效,即使工厂运行在五个不同的等级.
  • 该技术成功地预测了新引入的等级的产品质量,展示了其适应性.
  • 使用自适应子模型进行集体学习,与单个模型相比,显著提高了预测准确性.

    • 结论:

    • 开发的自适应软件传感器技术,利用转移学习和LW-PLS,为预测多级环境中的过程变量提供了强大的解决方案.
    • 该方法能够适应新的等级和不同的操作条件,这使其在工业应用中非常有价值.
    • 自动源域选择是防止负转移和最大化转移学习在工艺工业中的好处的关键.