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首页> 外文期刊>Advances in Mechanical Engineering >Fault diagnosis of bearing based on relevance vector machine classifier with improved binary bat algorithm for feature selection and parameter optimization:
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Fault diagnosis of bearing based on relevance vector machine classifier with improved binary bat algorithm for feature selection and parameter optimization:

机译：基于相关矢量机分类的轴承故障诊断，具有改进二元蝙蝠算法的特征选择和参数优化：

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In this article, fault diagnosis of bearing based on relevance vector machine classifier with improved binary bat algorithm is proposed, and the improved binary bat algorithm is used to select the appropriate features and kernel parameter of relevance vector machine. In the improved binary bat algorithm, the new velocities updating method of the bats is presented in order to ensure the decreasing of the probabilities of changing their position vectors’ elements when the position vectors’ elements of the bats are equal to the current best location’s element, and the increasing of the probabilities of changing their position vectors’ elements when the position vectors’ elements of the bats are unequal to the current best location’s element, which are helpful to strengthen the optimization ability of binary bat algorithm. The traditional relevance vector machine trained by the training samples with the unreduced features can be used to compare with the proposed improved binary bat algorithm–relevance vector ...
机译：在本文中，提出了基于相关的二进制BAT算法的相关矢量机分类器的轴承的故障诊断，并且改进的二进制BAT算法用于选择相关矢量机的适当特征和核参数。在改进的二进制BAT算法中，提出了蝙蝠的新速度更新方法，以确保当蝙蝠的位置矢量元件等于当前最佳位置的元素时，以减少改变其位置矢量元素的概率并且当蝙蝠的位置矢量元件对当前最佳位置的元件不相等时，增加改变其位置矢量元件的概率，这有助于增强二元BAT算法的优化能力。具有未更新的特征的训练样本训练的传统相关矢量机可用于与所提出的改进二进制BAT算法 - 相关矢量进行比较......

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