Nonlinear dynamical system identification using unscented Kalman filter

• Main Authors: Rehman, M.J.U., Dass, S.C., Asirvadam, V.S.
• Format: Conference or Workshop Item
• Published: American Institute of Physics Inc. 2016
• 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/

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...