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Processability Analysis using Principal Component Analysis and Support Vector Machine.

机译：使用主成分分析和支持向量机的可加工性分析。

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The obtained model developed outperforms the existing linear and logistic prediction methods in terms of content prediction error. As the proof of concept, the methodology is applied to an oil sands processing dataset created using an artificial model with such variables as bitumen content and fines content of ores, along with the processing variables such as pH and temperature.

机译：在内容预测误差方面，开发的获得的模型优于现有的线性和逻辑预测方法。作为概念证明，该方法适用于使用人工模型创建的油砂加工数据集，该模型具有诸如沥青的沥青含量和矿石细度的变量以及诸如pH和温度的加工变量。

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作者
Zhang, Yixin.;
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作者单位

University of Alberta (Canada).;

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授予单位 University of Alberta (Canada).;
学科 Electrical engineering.
学位 M.S.
年度 2014
页码 76 p.
总页数 76
原文格式 PDF
正文语种 eng
中图分类老年病学;
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Processability Analysis using Principal Component Analysis and Support Vector Machine.

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