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 language | English |
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| Title of host publication | Proceedings of the 24th PhD Mini-Symposium of the Department of Measurement and Information Systems, Budapest University of Technology and Economics |
| Place of Publication | Budapest |
| Pages | 74-77 |
| Number of pages | 4 |
| ISBN (Electronic) | 978-963-313-243-2 |
| Publication status | Published - 30 Jan 2017 |
| Externally published | Yes |
| Event | 24th PhD Mini-Symposium (MINISY@DMIS 2017) - Budapest University of Technology and Economics. Building I, Budapest, Hungary Duration: 30 Jan 2017 → 31 Jan 2017 http://minisy.mit.bme.hu/ |
Conference
| Conference | 24th PhD Mini-Symposium (MINISY@DMIS 2017) |
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| Country/Territory | Hungary |
| City | Budapest |
| Period | 30/01/17 → 31/01/17 |
| Internet address |