Robotics & Machine Learning Daily News2024,Issue(Jun.11) :70-70.

Investigators at Chinese Academy of Sciences Describe Findings in Machine Learni ng (Machine Learning Optimization for Catalytic Desulfurization of Petroleum: Mu lti-layered Perceptron, Multi Task Lasso, and Gaussian Process Regression Models)

中国科学院的研究人员描述了机器学习(石油催化脱硫的机器学习优化:多层感知器、多任务Lasso和高斯过程回归模型)的发现

Robotics & Machine Learning Daily News2024,Issue(Jun.11) :70-70.

Investigators at Chinese Academy of Sciences Describe Findings in Machine Learni ng (Machine Learning Optimization for Catalytic Desulfurization of Petroleum: Mu lti-layered Perceptron, Multi Task Lasso, and Gaussian Process Regression Models)

中国科学院的研究人员描述了机器学习(石油催化脱硫的机器学习优化:多层感知器、多任务Lasso和高斯过程回归模型)的发现

扫码查看

摘要

Robotics&Machine Learning Daily News的一位新闻记者兼工作人员新闻编辑每日新闻-机器学习的新数据在一份新的报告中呈现。根据《中华人民共和国合肥消息》,NewsRx记者报道,“本研究开发并优化了机器学习模型S,以优化加氢脱硫(HDS)燃料生产过程,并对多个输入参数进行了考虑和优化,以找到最佳响应参数。”

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning are pre sented in a new report. According to news originating from Hefei, People’s Repub lic of China, by NewsRx correspondents, research stated, “Machine learning model s were developed and optimized in this study to optimize the process of hydrodes ulfurization (HDS) for fuel production. A number of input parameters were consid ered and optimized to find the best response parameters.”

Key words

Hefei/People’s Republic of China/Asia/Cyborgs/Emerging Technologies/Gaussian Processes/Machine Learning/Perceptro n/Chinese Academy of Sciences

引用本文复制引用

出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
段落导航相关论文