Nonlinear system identification via basis functions based time domain volterra model
This paper proposes basis functions based time domain Volterra model for nonlinear system identification. The Volterra kernels are expanded by using complex exponential basis functions and estimated via genetic algorithm (GA). The accuracy and practicability of the proposed method are then assessed...
| Main Authors: | Yazid, E., Liew, M.S., Parman, S., Kurian, V.J., Ng, C.Y. |
|---|---|
| Format: | Conference or Workshop Item |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.32258 / |
| Published: |
EDP Sciences
2014
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| Online Access: |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904994059&doi=10.1051%2fmatecconf%2f20141302031&partnerID=40&md5=bf5c5cde61e94f06adf5713ccd61d418 http://eprints.utp.edu.my/32258/ |
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utp-eprints.322582022-03-29T05:02:40Z Nonlinear system identification via basis functions based time domain volterra model Yazid, E. Liew, M.S. Parman, S. Kurian, V.J. Ng, C.Y. This paper proposes basis functions based time domain Volterra model for nonlinear system identification. The Volterra kernels are expanded by using complex exponential basis functions and estimated via genetic algorithm (GA). The accuracy and practicability of the proposed method are then assessed experimentally from a scaled 1:100 model of a prototype truss spar platform. Identification results in time and frequency domain are presented and coherent functions are performed to check the quality of the identification results. It is shown that results between experimental data and proposed method are in good agreement. © 2014 Owned by the authors, published by EDP Sciences. EDP Sciences 2014 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904994059&doi=10.1051%2fmatecconf%2f20141302031&partnerID=40&md5=bf5c5cde61e94f06adf5713ccd61d418 Yazid, E. and Liew, M.S. and Parman, S. and Kurian, V.J. and Ng, C.Y. (2014) Nonlinear system identification via basis functions based time domain volterra model. In: UNSPECIFIED. http://eprints.utp.edu.my/32258/ |
| institution |
Universiti Teknologi Petronas |
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UTP Institutional Repository |
| description |
This paper proposes basis functions based time domain Volterra model for nonlinear system identification. The Volterra kernels are expanded by using complex exponential basis functions and estimated via genetic algorithm (GA). The accuracy and practicability of the proposed method are then assessed experimentally from a scaled 1:100 model of a prototype truss spar platform. Identification results in time and frequency domain are presented and coherent functions are performed to check the quality of the identification results. It is shown that results between experimental data and proposed method are in good agreement. © 2014 Owned by the authors, published by EDP Sciences. |
| format |
Conference or Workshop Item |
| author |
Yazid, E. Liew, M.S. Parman, S. Kurian, V.J. Ng, C.Y. |
| spellingShingle |
Yazid, E. Liew, M.S. Parman, S. Kurian, V.J. Ng, C.Y. Nonlinear system identification via basis functions based time domain volterra model |
| author_sort |
Yazid, E. |
| title |
Nonlinear system identification via basis functions based time domain volterra model |
| title_short |
Nonlinear system identification via basis functions based time domain volterra model |
| title_full |
Nonlinear system identification via basis functions based time domain volterra model |
| title_fullStr |
Nonlinear system identification via basis functions based time domain volterra model |
| title_full_unstemmed |
Nonlinear system identification via basis functions based time domain volterra model |
| title_sort |
nonlinear system identification via basis functions based time domain volterra model |
| publisher |
EDP Sciences |
| publishDate |
2014 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904994059&doi=10.1051%2fmatecconf%2f20141302031&partnerID=40&md5=bf5c5cde61e94f06adf5713ccd61d418 http://eprints.utp.edu.my/32258/ |
| _version_ |
1741197708427788288 |
| score |
11.62408 |