文章基本信息

标题：Generating Low-dimensional, Nonlinear Process Representations by Ordered Features Õ
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作者：Susanne Fischer ; Susanne Fischer ; Onno Hensgen 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2015
卷号：48
期号：3
页码：1037-1042
DOI：10.1016/j.ifacol.2015.06.220
语种：English
出版社：Elsevier
摘要：Abstract Reduced models of manufacturing processes are essential for online control of these processes. By generating a low-dimensional, nonlinear process representation, we extract process state features, ordered by relevance, that can be used for the construction of a reduced model. In this paper, we propose new approaches for nonlinear dimensionality reduction based on sequential bottleneck neural networks. The extracted features represent the state in a low-dimensional nonlinear subspace of the original process representation space. The feasibility of these approaches is shown with data of a numerically simulated deep drawing process.
关键词：KeywordsNonlinear dimensionality reductionneural networksdata compressionreductionregressionmanufacturing processes