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首页> 外文期刊>Mathematical Problems in Engineering >Recognition of Mixture Control Chart Pattern Using Multiclass Support Vector Machine and Genetic Algorithm Based on Statistical and Shape Features
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Recognition of Mixture Control Chart Pattern Using Multiclass Support Vector Machine and Genetic Algorithm Based on Statistical and Shape Features

机译：基于统计和形状特征的多类支持向量机和遗传算法的混合控制图模式识别

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摘要

Control charts have been widely utilized for monitoring process variation in numerous applications. Abnormal patterns exhibited by control charts imply certain potentially assignable causes that may deteriorate the process performance. Most of the previous studies are concerned with the recognition of single abnormal control chart patterns (CCPs). This paper introduces an intelligent hybrid model for recognizing the mixture CCPs that includes three main aspects: feature extraction, classifier, and parameters optimization. In the feature extraction, statistical and shape features of observation data are used in the data input to get the effective data for the classifier. A multiclass support vector machine (MSVM) applies for recognizing the mixture CCPs. Finally, genetic algorithm (GA) is utilized to optimize the MSVM classifier by searching the best values of the parameters of MSVM and kernel function. The performance of the hybrid approach is evaluated by simulation experiments, and simulation results demonstrate that the proposed approach is able to effectively recognize mixture CCPs.
机译：控制图已被广泛用于监视众多应用中的过程变化。控制图显示的异常模式表示某些潜在的可分配原因，可能会导致过程性能下降。先前的大多数研究都与单个异常控制图模式（CCP）的识别有关。本文介绍了一种用于识别混合CCP的智能混合模型，该模型包括三个主要方面：特征提取，分类器和参数优化。在特征提取中，将观测数据的统计特征和形状特征用于数据输入，以获取分类器的有效数据。多类支持向量机（MSVM）用于识别混合CCP。最后，利用遗传算法（GA）通过搜索MSVM参数和内核函数的最佳值来优化MSVM分类器。仿真实验评估了混合方法的性能，仿真结果表明，该方法能够有效识别混合CCP。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第19期|382395.1-382395.10|共10页
  • 作者

    Zhang Min; Cheng Wenming;

  • 作者单位

    Southwest Jiaotong Univ, Sch Mech Engn, Chengdu 610031, Peoples R China.;

    Southwest Jiaotong Univ, Sch Mech Engn, Chengdu 610031, Peoples R China.;

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