使用深度学习的软传感器建模,具有最大的相关性和最小的冗余性,用于工业过程的质量预测

Huaiping Jin1, Xin Dong2, Bin Qian1

  • 1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China; The Higher Educational Key Laboratory for Industrial Intelligence and Systems of Yunnan Province, Kunming University of Science and Technology, Kunming 650500, China.

ISA transactions
|February 17, 2025
PubMed
概括

基于最大相关性和最小冗余性的表示学习 (MRMRRL) 通过增强特征提取和减少冗余性来改善软传感器质量预测. 这种深度学习方法在工业应用中明显优于传统方法.