新型半监督稀疏堆叠自动编码器集成与局部线性嵌入,用于工业软传感
Yan-Lin He1, Yu Jiang1, Hui-Hui Gao2
1College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, 100029, China.
ISA transactions
|June 14, 2025
概括
这项研究介绍了一种新的半监督稀疏堆叠自动编码器与局部线性嵌入 (SS-SAE-LLE) 工业软传感器建模. 该方法通过捕获时空数据特征来提高复杂过程的预测准确性.
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:
- 工业过程产生具有时间依赖性和高维度的复杂数据,挑战传统的软传感.
- 现有的软传感器模型与复杂的工业数据特征作斗争,限制了预测准确度.
研究的目的:
- 提出一种与局部线性嵌入 (SS-SAE-LLE) 集成的新型半监督稀疏堆叠自动编码器,用于增强的工业软传感器建模.
- 解决传统自动编码器在捕获时空数据特征方面的局限性,并提高预测准确性.
主要方法:
- SS-SAE-LLE算法将一个半监督的堆叠自动编码器与局部线性嵌入算法相结合.
- 它利用层次特征提取,时空数据特征和使用标记数据的监督调整.
- 该模型在半监督学习框架内接受培训.
主要成果:
- 对PTA溶剂和SRU系统数据集的实验证明了SS-SAE-LLE的有效性.
- 与现有模型相比,拟议的方法实现了更高的预测准确性.
- SS-SAE-LLE有效地处理工业过程数据的时空特征.
结论:
- SS-SAE-LLE为工业软传感器建模提供了强大的解决方案,性能优于传统方法.
- 局部线性嵌入的集成增强了模型捕获复杂数据结构的能力.
- 这些发现突显了SS-SAE-LLE在现实工业环境中的适用性和改进性能.
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