Objective A novel technique for the identification of gas-to-liquid (GTL) lubricants based on mutual information method and Bayesian algorithm is proposed to enhance the tariff identification of lubricating oils. Methods First, feature parameters of GTL lubricants and conventional petroleum lubricants are extracted, and the mutual information algorithm is used to select feature indicators with discriminative ability. Then, the Bayesian algorithm is used to model the selected feature indicators, which finally achieves the classification discrimination of GTL lubricants and conventional lubricants. Results The experimental results demonstrate the effectiveness of the proposed method in accurately discriminating between GTL lubricants and conventional petroleum lubricants. Conclusions The established model exhibits good stability and reliability, providing a comprehensive and accurate identification tool for the lubricating oil industry and tariff assessment. [ABSTRACT FROM AUTHOR]

目的 为完善润滑油的关税鉴定,提出了一种基于互信息和贝叶斯算法的天然气合成润滑油鉴别技术。方法 首先,提 取了天然气合成润滑油和常规矿物油基润滑油的特征参数,并运用互信息方法对其进行特征指标选择,筛选出具有鉴别能力的特 征指标。然后,对所选特征指标采用贝叶斯算法进行建模,最终实现了天然气合成润滑油和常规矿物润滑油的分类鉴别。结果 实 验结果表明,该方法所建立的模型能够有效鉴别天然气合成润滑油和常规矿物润滑油。结论 该方法所建立的模型具有良好的稳 定性和可靠性,为润滑油行业和关税鉴定提供了一种全面和准确的鉴别工具。 [ABSTRACT FROM AUTHOR]

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