对于域自适应回归的非线性动态转移部分最小平方
Zhijun Zhao1, Gaowei Yan2, Mifeng Ren1
1College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, 030024, Shanxi, China.
ISA transactions
|August 14, 2024
概括
这项研究引入了一种新的非线性动态转移软传感器算法,以应对在不断变化的条件下模型退化. 该方法有效地处理复杂的数据,改善各种应用中的软传感器性能.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 过程控制 过程控制
- 数据科学数据科学数据科学
背景情况:
- 软传感器模型在不断变化的工作条件下往往会退化.
- 动态,非线性和多模式数据对传统软传感器构成挑战.
研究的目的:
- 开发一个强大的非线性动态转移软传感器算法.
- 为了解决模型退化问题,并适应复杂的数据特征.
主要方法:
- 时间延迟数据增强用于捕获系统动态.
- 隐藏空间投影与分布对齐和第一阶差异规范化.
- 拉普拉斯规范化用于非线性回归中的几何结构保存.
- 部分最小平方优化框架与贝叶斯式超参数调整.
主要成果:
- 拟议的算法在三个公共数据集上表现出有效性.
- 成功地解决了数据分布的差异,并保留了几何结构.
- 在不断变化的工作条件下提高软传感器性能.
结论:
- 非线性动态转移软传感器算法为工业应用提供了强大的解决方案.
- 该方法提高了软传感器在动态环境中的适应性和可靠性.
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