relation: https://khub.utp.edu.my/scholars/7183/
title: Markov chain Monte Carlo (MCMC) method for parameter estimation of nonlinear dynamical systems
creator: Ur Rehman, M.J.
creator: Dass, S.C.
creator: Asirvadam, V.S.
description: This manuscript is concerned with parameter estimation of nonlinear dynamical system. Bayesian framework is very useful for parameter estimation, Metropolis-Hastings (MH) algorithm is proposed for constructing the posterior density, which is main working procedure of Bayesian analysis. Extended Kalman Filter (EKF) gives better results in non-linear environment at each time step in which Taylor series approximation for nonlinear system is used. A performance comparison of EKF in linear and non-linear environment is proposed. This study will give us the solution for nonlinear systems, numerical integration of complex integrals and parameter estimation of stochastic differential equations (SDE). © 2015 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2016
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Ur Rehman, M.J. and Dass, S.C. and Asirvadam, V.S.  (2016) Markov chain Monte Carlo (MCMC) method for parameter estimation of nonlinear dynamical systems.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971644702&doi=10.1109%2fICSIPA.2015.7412154&partnerID=40&md5=5fbb87ef138e915bb6c4253b8846dc88
relation: 10.1109/ICSIPA.2015.7412154
identifier: 10.1109/ICSIPA.2015.7412154