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A bilinear identification‐modeling framework from time domain data
Author(s) -
Karachalios Dimitrios S.,
Gosea Ion Victor,
Antoulas Athanasios C.
Publication year - 2019
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.201900246
Subject(s) - bilinear interpolation , reduction (mathematics) , nonlinear system , computer science , identification (biology) , interpolation (computer graphics) , system identification , domain (mathematical analysis) , time domain , algorithm , frequency domain , nonlinear system identification , data mining , mathematical optimization , mathematics , measure (data warehouse) , artificial intelligence , motion (physics) , mathematical analysis , physics , geometry , botany , quantum mechanics , computer vision , biology
An ever‐increasing need for improving the accuracy includes more involved and detailed features, thus inevitably leading to larger‐scale dynamical systems [1]. To overcome this problem, efficient finite methods heavily rely on model reduction. One of the main approaches to model reduction of both linear and nonlinear systems is by means of interpolation. The Loewner framework is a direct data‐driven method able to identify and reduce models derived directly from measurements. For measured data in the frequency domain, the Loewner framework is well established in linear case [2] while it has already extended to nonlinear [6]. On the other hand in the case of time domain data, the Loewner framework was already applied for approximating linear models [3]. In this study, an algorithm which uses time domain data for nonlinear (bilinear) system reduction and identification is presented.