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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.