基于PCA-ELM算法的注塑成形工艺参数优化  

Technical Parametric Optimization of Plastic Injection Forming by PCA-ELM Algorithm

作　　者：徐承亮[1] 曹志勇[2] 王大军[3] 胡吉全[4] XU Chengliang;CAO Zhiyong;WANG Dajun;HU Jiquan(Information Engineering School,Guangzhou Vocational College of Technology and Business,Guangzhou Guangdong 511442,China;School of Material Science and Engineering,Hubei University,Wuhan Hubei 430074,China;College of Automation,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;Logistics Engineering College,Wuhan University of Technology,Wuhan Hubei 430072,China)

机构地区：[1]广州科技贸易职业学院信息工程学院,广东广州511442 [2]湖北大学材料科学与工程学院,湖北武汉430074 [3]重庆邮电大学自动化学院,重庆400065 [4]武汉理工大学物流工程学院,湖北武汉430072

出　　处：《机床与液压》2018年第21期117-122,149,共7页Machine Tool & Hydraulics

基　　金：国家自然科学基金面上项目(51675201);模具技术国家重点实验室开放基金(P2018-006)

摘　　要：引入了一种特征提取方法——主成分分析法(PCA),通过降维,把高维的决策变量映射到低维空间,得到主成分(主要的决策变量),简化了模型,提高了效率,并结合极限学习机(ELM),使用一种改进的带精英策略的非支配排序遗传算法(NSGAⅡ),对建立的多目标优化模型进行求解,得到Pareto最优解集。A feature extraction method of principal component analysis （PCA）was introduced. By the application of dimension reduction , the decision variables were mapped from the high-dimensional to the low dimensional one, and the principal components （main decision variables） were obtained. The simplified model with improve efficiency combined with Extreme Learning Machine （ELM） was constructed. The multi-objective optimization model was established by using of an improved strategy of non-dominated sorting genetic algorithm （NSGA II）. Pareto optimal solution sets were solved.

关 键 词：主成分分析 极限学习机 非支配排序遗传算法 PARETO最优解 

分 类 号：TS943.66[轻工技术与工程—服装设计与工程]