Nonlinear dynamical system identification using unscented Kalman filter
Kalman Filter is the most suitable choice for linear state space and Gaussian error distribution from decades. In general practical systems are not linear and Gaussian so these assumptions give inconsistent results. System Identification for nonlinear dynamical systems is a difficult task to perform...
| Main Authors: | Rehman, M.J.U., Dass, S.C., Asirvadam, V.S. |
|---|---|
| Format: | Conference or Workshop Item |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.30637 / |
| Published: |
American Institute of Physics Inc.
2016
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| Online Access: |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85005982804&doi=10.1063%2f1.4968052&partnerID=40&md5=1ec8ef8fd2abdc8a50f1131227abb3c5 http://eprints.utp.edu.my/30637/ |
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