首页|Data from University of Arizona Advance Knowledge in Machine Learning (Large Lan guage Model-based Interpretable Machine Learning Control In Building Energy Syst ems)
Data from University of Arizona Advance Knowledge in Machine Learning (Large Lan guage Model-based Interpretable Machine Learning Control In Building Energy Syst ems)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Machine Learning are discussed in a new report. According to news reporting originating in Tucson , Arizona, by NewsRx journalists, research stated, “The potential of Machine Lea rning Control (MLC) in HVAC systems is hindered by its opaque nature and inferen ce mechanisms, which is challenging for users and modelers to fully comprehend, ultimately leading to a lack of trust in MLC-based decision -making. To address this challenge, this paper investigates and explores Interpretable Machine Learn ing (IML), a branch of Machine Learning (ML) that enhances transparency and unde rstanding of models and their inferences, to improve the credibility of MLC and its industrial application in HVAC systems.”
TucsonArizonaUnited StatesNorth an d Central AmericaCyborgsEmerging TechnologiesMachine LearningUniversity of Arizona
NETL
NSTL
