文献类型：期刊文献

英文题名：A genetic neural fuzzy system and its application in quality prediction in the injection process

作者：Li, Erguo[1]; Jia, Li[1]; Yu, Jinshou[1]

机构：[1]E China Univ Sci & Technol, Res Inst Automat, Shanghai 200237, Peoples R China

年份：2004

卷号：191

期号：3

起止页码：335

外文期刊名：CHEMICAL ENGINEERING COMMUNICATIONS

收录：;EI(收录号：2004148099039);WOS:【SCI-EXPANDED(收录号:WOS:000188772100002)】；

语种：英文

外文关键词：genetic algorithm; BP algorithm; neural fuzzy system; quality prediction; injection process

摘要：A genetic neural fuzzy system (GNFS) is presented and introduced to quality prediction in the injection process. A hybrid-learning algorithm is proposed, which is divided into two stages to train GNFS. During the first learning stage, the genetic algorithm is used to optimize the structure of GNFS and the membership function of each fuzzy term because of its capability of parallel and global search. On the basis of the first optimized training stages, the back-propagation algorithm (BP algorithm) is adopted to update the parameters of the GNFS to improve its predicting precision and reduce the computation time. The process of constructing a quality prediction model for an injection process based on GNFS is described in detail. The predicted weight of the molded part from the model based on GNFS demonstrates that the proposed GNFS has superior performance and good generalization capability in quality prediction in the injection process.