摘要
由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-研究人员详细介绍机器学习的新数据。根据新闻报道在德国马格德堡，新sRx记者的研究表明，“非球形混合旋转鼓中的颗粒表现出显著的复杂性，特别是当密度分离和尺寸分离同时发生。三种机器学习模型：人工神经网络(ANN)，极随机树(ERT)和粒子群优化支持向量回归(PSO-SVR)用于预测棒状颗粒在稳态混合状态下的混合时间和混合程度腐烂的鼓。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Researchers detail new data in Machine Learning. According to news reporting originatingin Magdeburg, Germany, by New sRx journalists, research stated, “The mixing of non-sphericalparticles in rota ry drums exhibits significant complexity, particularly when density segregation and sizesegregation occur simultaneously. Three machine learning models: artifi cial neural network (ANN), extremelyrandomized trees (ERT), and particle swarm optimized support vector regression (PSO-SVR) weredeveloped to predict the mixi ng time and mixing degree at the steady mixing state of rodlike particles inrot ary drums.”
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