Block-oriented identification using the best linear approximation: benefits and drawbacks

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Abstract

Due to their simplicity and structured nature, block-oriented models are popular in nonlinear modeling applications. A wide range of block-oriented identification algorithms were developed over the years. One class of these approaches uses the so-called best linear approximation to initialize the identification algorithm. The best linear approximation framework allows the user to extract important information about the system, it guides the user in selecting good candidate model structures and orders, and it proves to be a good starting point for nonlinear system identification algorithms. This paper gives an overview of the benefits and drawbacks of using identification algorithms based on the best linear approximation.
Original languageEnglish
Title of host publicationProceedings of the 24th PhD Mini-Symposium of the Department of Measurement and Information Systems, Budapest University of Technology and Economics
Place of PublicationBudapest
Pages74-77
Number of pages4
ISBN (Electronic)978-963-313-243-2
Publication statusPublished - 30 Jan 2017
Externally publishedYes
Event24th PhD Mini-Symposium (MINISY@DMIS 2017) - Budapest University of Technology and Economics. Building I, Budapest, Hungary
Duration: 30 Jan 201731 Jan 2017
http://minisy.mit.bme.hu/

Conference

Conference24th PhD Mini-Symposium (MINISY@DMIS 2017)
CountryHungary
CityBudapest
Period30/01/1731/01/17
Internet address

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