摘要
由一名新闻记者兼机器人与机器学习每日新闻编辑-研究人员详细介绍了机器学习的新数据。根据NewsRx Editor S在法国格勒诺布尔的新闻报道，研究表明，“先进而准确的岩石破碎分布预测可以减少二次破碎工作，降低人工设备成本，提高效率，从而使隧道开挖朝着轻量化方向发展。为此，提出了一种新的混合随机森林(RF)模型，该模型由原子轨道AL Search(AOS)和逻辑映射(LM)优化，即LMAOS-RF，建议Pred ICT岩石尺寸分布。本研究的资助单位包括国家自然科学基金(NSFC)、湖南省杰出青年科学基金、中国学术委员会。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning. According to news reporting out of Grenoble, France, by NewsRx editor s, research stated, “Advanced and accurate prediction of rock fragmentation dist ribution can reduce the secondary crushing work, the cost of manual equipment an d increase efficiency, thereby enabling tunnel excavation towards lightweighting . To that end, a novel hybrid random forest (RF) model optimized by atomic orbit al search (AOS) with Logistic mapping (LM), i.e., LMAOS-RF, was proposed to pred ict rock size distribution.” Funders for this research include National Natural Science Foundation of China ( NSFC), Distinguished Youth Sci- ence Foundation of Hunan Province of China, Chin a Scholarship Council.
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