计算机应用研究2012，Vol.29Issue(12)：4538-4541,4545,5.DOI:10.3969/j.issn.1001-3695.2012.12.035
基于遗传优化的PCA-SVM控制图模式识别
PCA-SVM for control chart recognition of genetic optimization
摘要
Abstract
Considering the problem that the precision and generalization are not ideal when recognize the basic patterns of quality control chart in PCA and PCA-SVM modeling, this paper proposed a control chart pattern recognition method based on genetic algorithm and PCA-SVM. The basic idea of the method was that, firstly, in view of the dimensionality reduction in feature space, used principal component analysis algorithm to lower the sample dimension, it also highlighted the clustering features. Then regarded the component characteristics as a chromosome which was then performed with binary code. It used a support vector machine classifier to recognized a random chromosome and considered recognition accuracy as the fitness function to evaluate the fitness of individual feature. By the operations of selection, crossover and mutation, with GA self-adaptive optimizing for penalty parameter and kernel parameter. Finally, it introduced the optimized SVM modeling to identify the control chart pattern. The simulation experimental results demonstrate that the proposed method has higher detection accuracy and stronger generalization ability than other methods, so it is more suitable for quality control in production field.关键词
控制图/模式识别/遗传优化/主元分析/支持向量机Key words
control chart/ pattern recognition/ genetic optimization/ principal component analysis ( PCA) / support vector machine (SVM)分类
信息技术与安全科学引用本文复制引用
李太福,胡胜,魏正元,韩亚军..基于遗传优化的PCA-SVM控制图模式识别[J].计算机应用研究,2012,29(12):4538-4541,4545,5.基金项目
国家自然科学基金资助项目(61174015,51075418) （61174015,51075418）
重庆市自然科学基金资助项目(CSTC2010BB2285) （CSTC2010BB2285）
