文章基本信息

标题：Order Determination and Input Selection with Local Model Networks
本地全文：下载
作者：Julian Belz ; Oliver Nelles ; Daniel Schwingshackl 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：7327-7332
DOI：10.1016/j.ifacol.2017.08.1475
语种：English
出版社：Elsevier
摘要：AbstractAn automatic order determination and input selection method for nonlinear dynamic models following the external dynamics approach is proposed. Based on a wrapperlike input selection approach using local model networks (LMN), the proposed method is able to automatically identify important operating point (OP) variables solely based on measured data. If locallinearmodels are used, the OP variables describe regions in which the process can sufficiently be described by linear, dynamic models. The necessary order of these linear, dynamic models is determined altogether with the OP variables in one framework by the proposed method. We show that this method is able to improve the model accuracy and lead to a more concise model for a real-world heating, ventilating and air conditioning (HVAC) system.
关键词：KeywordsNonlinear system identificationsystem orderdynamic modelsnonlinear modelsneural networksinput selectiondynamic model order determination