eprintid: 7183
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/71/83
datestamp: 2023-11-09 16:18:59
lastmod: 2023-11-09 16:18:59
status_changed: 2023-11-09 16:08:41
type: conference_item
metadata_visibility: show
creators_name: Ur Rehman, M.J.
creators_name: Dass, S.C.
creators_name: Asirvadam, V.S.
title: Markov chain Monte Carlo (MCMC) method for parameter estimation of nonlinear dynamical systems
ispublished: pub
keywords: Differential equations; Dynamical systems; Extended Kalman filters; Image processing; Markov processes; Monte Carlo methods; Nonlinear analysis; Nonlinear dynamical systems; Nonlinear equations; Nonlinear systems; Numerical methods; Stochastic systems, Bayesian; Bayesian frameworks; Markov chain Monte Carlo method; Numerical integrations; Parameter; Performance comparison; Stochastic differential equations; Taylor series approximation, Parameter estimation
note: cited By 2; Conference of 4th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2015 ; Conference Date: 19 October 2015 Through 21 October 2015; Conference Code:119504
abstract: 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.
date: 2016
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971644702&doi=10.1109%2fICSIPA.2015.7412154&partnerID=40&md5=5fbb87ef138e915bb6c4253b8846dc88
id_number: 10.1109/ICSIPA.2015.7412154
full_text_status: none
publication: IEEE 2015 International Conference on Signal and Image Processing Applications, ICSIPA 2015 - Proceedings
pagerange: 7-10
refereed: TRUE
isbn: 9781479989966
citation:   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.