工业工程2012，Vol.15Issue(5)：125-129,5.DOI:10.3969/j.issn.1007-7375.2012.05.021
基于自适应粒子群算法和支持向量机的控制图模式识别
Recognition of Control Chart Pattern by Using Adaptive Mutation Particle Swarm Optimization and Support Vector Machine
摘要
Abstract
Due to the complexity of production processes resulting from multi -item production, effective production control is necessary. For this purpose, an intelligent control chart pattern recognition method is proposed. This method can improve the recognition accuracy by using adaptive mutation particle swarm optimization (AMPSO ) and support vector machine ( SVM) classifier. It uses one - against - one SVM multi - class classifier to recognize the control patterns because of its excellent small sample learning- Meanwhile, AMPSO is used to optimize the parameters of SVM kernel function. 20 - dimension simulated data sets of ten control chart patterns, including six fundamental patterns and four mix patterns, are used to test the proposed method. Also, it is compared with BP, SVM, and PSO - SVM methods. Simulation results show that the proposed method can get high recognition accuracy, which is up to 98.14% , while it is 75% if BP is applied. This implies that it is a feasible way to recognize control chart pattern in practice.关键词
控制图/模式识别/支持向量机/粒子群Key words
control chart/ pattern recognition/ support vector machine/ particle swarm optimization分类
机械制造引用本文复制引用
张敏,程文明..基于自适应粒子群算法和支持向量机的控制图模式识别[J].工业工程,2012,15(5):125-129,5.基金项目
中央高校基本科研业务费专项资金专题研究项目(2010ZT03) （2010ZT03）
国家自然科学基金资助项目(51175442) （51175442）
