Quality Prediction of Plasticizing and Molding Process of Single-Based Gun Propellant Based on GG-KECA-RVM Multi-Stage Model Fusion
Yang MY(杨明毅)1,2,3; Xu ZG(徐志刚)1; Wang JY(王军义i1; Yu TJ(余廷江)4; Chen SB(陈舒渤)4
2022
会议日期February 10-12, 2022
会议地点Virtual, Munich, Germany
关键词kernel entropy component analysis local latent variable modeling plasticizing and molding process relevance vector machine single-based gun propellant
页码12-20
英文摘要Aiming at the non-linear, multi-stage and high dimension characteristics of the plasticizing and molding process of single-based gun propellant, a quality prediction method based on GG-KECA-RVM multi-stage model fusion is proposed. The method is based on Gath-Geva dynamic fuzzy clustering to identify the stages of the plasticizing and molding process. KECA is introduced for deep feature extraction in each stage, and the local latent variable regression models based on KECA-RVM are established for each sub-stage. Finally, the fuzzy membership degree of Gath-Geva clustering is used to fuse the prediction results of multiple local models, which reflects the difference and cumulative characteristics of each stage on the quality, and realizes the accurate prediction of stage quality and process endpoint quality. The experimental results of the plasticizing and molding process show the effectiveness of the proposed method.
产权排序1
会议录2022 8th International Conference on Mechatronics and Robotics Engineering, ICMRE 2022
会议录出版者IEEE
会议录出版地New York
语种英语
ISBN号978-1-6654-8377-3
内容类型会议论文
源URL[http://ir.sia.cn/handle/173321/30840]  
专题沈阳自动化研究所_装备制造技术研究室
通讯作者Yang MY(杨明毅)
作者单位1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
2.University of Chinese Academy of Sciences, Beijing, China
3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
4.Luzhou North Chemical Industries Co. Ltd., Luzhou, China
推荐引用方式
GB/T 7714
Yang MY,Xu ZG,Wang JY(王军义i,et al. Quality Prediction of Plasticizing and Molding Process of Single-Based Gun Propellant Based on GG-KECA-RVM Multi-Stage Model Fusion[C]. 见:. Virtual, Munich, Germany. February 10-12, 2022.
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