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首页> 外文期刊>International Journal of Production Research >Kernel distance-based robust support vector methods and its application in developing a robust K-chart
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Kernel distance-based robust support vector methods and its application in developing a robust K-chart

机译：基于核距离的鲁棒支持向量法及其在鲁棒K图开发中的应用

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Traditional statistical process control (SPC) techniques are not applicable in many process industries due to autocorrelation among data. In addition, most conventional charts are based on the assumption that quality characteristics follow a multivariate normality assumption. Therefore, the reduction in process variability obtained through the use of SPC techniques has not been realized in the industries. However, this may not be reasonable in many real-world problems and its extension poses serious limitations. Hence, it is not only desirable, but also inevitable to have some techniques that can serve the same purpose as SPC control charts used for correlated parameters. In this paper, a robust support vector method drawn from statistical learning theory was applied to develop a multivariate control chart based on kernel distance, which is a measure of the distance between the centre of a class and the sample to be monitored. The proposed robust chart takes advantage of information extracted from in-control preliminary samples. A robust support vector method-based chart aims to solve the over fitting problems when outliers exist in the training data set. The robust support vector method makes the decision function less sensitive towards the noise and outliers. The performance of the robust chart is tested on the problem taken from the literature and the results verify the effectiveness of the chart and validate that the robust chart is better than the conventional charts when the distribution of the quality characteristics is not multivariate normal. Experiments for the problem undertaken confirm the reduction in the number of support vectors and there is significant improvement in performance when compared with the standard support vector methods.
机译：由于数据之间的自相关，传统的统计过程控制（SPC）技术不适用于许多过程行业。此外，大多数常规图表均基于质量特征遵循多元正态性假设的假设。因此，在工业中尚未实现通过使用SPC技术获得的过程可变性的降低。但是，这在许多现实世界中的问题中可能并不合理，并且其扩展构成了严重的局限性。因此，不仅希望，而且不可避免地要具有一些能够与用于相关参数的SPC控制图具有相同目的的技术。在本文中，采用了一种基于统计学习理论的鲁棒支持向量法来开发基于核距离的多变量控制图，该核距离是对类中心与要监视的样本之间的距离的度量。所提出的鲁棒图利用了从控制中的初步样本中提取的信息。基于鲁棒支持向量法的图表旨在解决训练数据集中存在离群值的过拟合问题。鲁棒的支持向量法使决策函数对噪声和异常值的敏感性降低。针对来自文献的问题对鲁棒图的性能进行了测试，结果验证了该图的有效性，并验证了当质量特征的分布不是多元正态分布时，鲁棒图优于常规图。针对该问题的实验证实，与标准支持向量方法相比，支持向量的数量减少了，并且性能有了显着提高。

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