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Least-Squares Suppo...
Least-Squares Support Vector Machines for the identification of Wiener-Hammerstein systems
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Falck, Tillmann (författare)
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Dreesen, Philippe (författare)
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De Brabanter, Kris (författare)
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- Pelckmans, Kristiaan (författare)
- Uppsala universitet,Avdelningen för systemteknik,Reglerteknik
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De Moor, Bart (författare)
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Suykens, Johan A. K. (författare)
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(creator_code:org_t)
- Elsevier BV, 2012
- 2012
- Engelska.
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Ingår i: Control Engineering Practice. - : Elsevier BV. - 0967-0661 .- 1873-6939. ; 20:11, s. 1165-1174
- Relaterad länk:
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https://lirias.kuleu...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- This paper considers the identification of Wiener-Hammerstein systems using Least-Squares Support Vector Machines based models. The power of fully black-box NARX-type models is evaluated and compared with models incorporating information about the structure of the systems. For the NARX models it is shown how to extend the kernel-based estimator to large data sets. For the structured model the emphasis is on preserving the convexity of the estimation problem through a suitable relaxation of the original problem. To develop an empirical understanding of the implications of the different model design choices, all considered models are compared on an artificial system under a number of different experimental conditions. The obtained results are then validated on the Wiener-Hammerstein benchmark data set and the final models are presented. It is illustrated that black-box models are a suitable technique for the identification of Wiener-Hammerstein systems. The incorporation of structural information results in significant improvements in modeling performance.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
Nyckelord
- Nonlinear system identification
- LS-SVMs
- Kernel-based models
- Overparameterization
- Large-scale data processing
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- art (ämneskategori)
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