Robotics & Machine Learning Daily News2024,Issue(Mar.15) :63-64.

Research Data from Fuzhou University Update Understanding of Support Vector Mach ines (Fault Section Location In Resonant Grounding Distribution Systems Based On Feature Subset Optimization of Phase Current Variation)

Robotics & Machine Learning Daily News2024,Issue(Mar.15) :63-64.

Research Data from Fuzhou University Update Understanding of Support Vector Mach ines (Fault Section Location In Resonant Grounding Distribution Systems Based On Feature Subset Optimization of Phase Current Variation)

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Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Research findings on Support Vector Machines are discussed in a new report. According to news reporting originating from Fuzhou, People’s Republic of China, by NewsRx correspondents, research stated, “The exis ting single-phase grounding (SPG) fault section location methods typically suffe r from difficulty in feature selection, limited feeder terminal units (FTUs) con figuration, and excessive dependence on communication, which weaken their genera lization and robustness. To overcome these challenges, an SPG fault section loca tion approach based on feature subset optimization is proposed.”

Key words

Fuzhou/People’s Republic of China/Asia/Algorithms/Emerging Technologies/Genetic Algorithms/Machine Learning/Suppo rt Vector Machines/Vector Machines/Fuzhou University

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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