协作多方参与者解决批量流程质量预测中的标签度问题
Ling Zhao1, Zheng Zhang2, Jinlin Zhu3
1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
这项研究引入了一个新的多参与者联合培训框架,以解决软传感器建模中的标签稀疏性. 该方法有效地利用未标记的数据来改善实时流程控制.
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
- 过程控制 过程控制
背景情况:
- 软传感器对于在先进控制中实时跟踪过程变量至关重要.
- 在软传感器建模中,标签稀缺性构成了重大挑战,限制了模型性能.
- 现有的方法很难有效地利用有限的标记数据.
研究的目的:
- 开发一个改进的软传感器建模框架,解决标签稀疏性问题.
- 调查软传感器开发多人联合培训方法的有效性.
- 在批量过程建模中增强未标记数据的利用.
主要方法:
- 提出了一种新的多人联合训练技术,扩展了传统的两人计划.
- 采用滑动窗口方法来捕获批量过程数据中的2D相关性.
- 该框架旨在有效地利用一小部分标记数据.
主要成果:
- 拟议的多参与者联合培训框架与现有方法相比,表现优越.
- 当未标记数据的比例增加时,有效性特别明显.
- 两个案例研究验证了开发的框架的实际适用性和稳定性.
结论:
- 多玩家联合培训框架为克服软传感器建模中的标签稀疏性提供了一个有希望的解决方案.
- 这种方法提高了软传感器在工业过程中的预测准确度和效率.
- 该研究强调了通过先进的机器学习技术利用未标记数据的潜力.
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