An $$L_{2,1}$$-regularized cascaded broad learning framework for robust anomaly detection in complex industrial metrology

IF 4.6 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Complex & Intelligent Systems Pub Date : 2026-04-17 DOI:10.1007/s40747-026-02316-1

Jianghao Lin, Zongze Wu, Zhigang Ren, Aimin Yang

引用次数: 0

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用于复杂工业计量中鲁棒异常检测的$$L_{2,1}$$ -正则级联广义学习框架

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来源期刊

Complex & Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-

CiteScore

9.60

自引率

10.30%

发文量

297

期刊介绍： Complex & Intelligent Systems aims to provide a forum for presenting and discussing novel approaches, tools and techniques meant for attaining a cross-fertilization between the broad fields of complex systems, computational simulation, and intelligent analytics and visualization. The transdisciplinary research that the journal focuses on will expand the boundaries of our understanding by investigating the principles and processes that underlie many of the most profound problems facing society today.