Application of the PSO-SVM model for recognition of control chart patterns
Application of the PSO-SVM model for recognition of control chart patterns
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DOI：
10.1016/j.isatra.2010.06.005
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发表时间：
2010-10-01
期刊：
影响因子：
7.3
通讯作者：
Ghaderi, Reza
中科院分区：
文献类型：
--
作者：
Ranaee, Vahid;Ebrahimzadeh, Ata;Ghaderi, Reza
控制图模式是重要的统计过程控制工具，用于确定过程是否以预期模式运行或存在不自然的模式。控制图模式的准确识别对于有效的系统监控以保持高质量的产品至关重要。本文介绍了一种新型的混合智能系统，包括三个主要模块：特征提取模块，分类器模块，优化模块。在特征提取模块中，提出了一种将形状特征和统计特征相结合的特征集作为模式的有效特征。在分类器模块中，提出了一种基于多类支持向量机（SVM）的分类器。对于优化模块，提出了一种粒子群优化算法，以提高识别器的泛化性能。在该模块中，SVM分类器设计通过搜索调整其判别函数（核参数选择）的参数的最佳值来优化，并且上游通过寻找馈送分类器的特征的最佳子集来优化。仿真结果表明，该算法具有很高的识别精度。这种高效率是实现只有很少的功能，这已经选择使用粒子群优化。(C)2010年伊萨。由爱思唯尔有限公司出版。保留所有权利。
Control chart patterns are important statistical process control tools for determining whether a process is run in its intended mode or in the presence of unnatural patterns. Accurate recognition of control chart patterns is essential for efficient system monitoring to maintain high-quality products. This paper introduces a novel hybrid intelligent system that includes three main modules: a feature extraction module, a classifier module, and an optimization module. In the feature extraction module, a proper set combining the shape features and statistical features is proposed as the efficient characteristic of the patterns. In the classifier module, a multi-class support vector machine (SVM)-based classifier is proposed. For the optimization module, a particle swarm optimization algorithm is proposed to improve the generalization performance of the recognizer. In this module, it the SVM classifier design is optimized by searching for the best value of the parameters that tune its discriminant function (kernel parameter selection) and upstream by looking for the best subset of features that feed the classifier. Simulation results show that the proposed algorithm has very high recognition accuracy. This high efficiency is achieved with only little features, which have been selected using particle swarm optimizer. (C) 2010 ISA. Published by Elsevier Ltd. All rights reserved.