%0 Conference Paper
%A Ur Rehman, M.J.
%A Dass, S.C.
%A Asirvadam, V.S.
%D 2016
%F scholars:7183
%I Institute of Electrical and Electronics Engineers Inc.
%K 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
%P 7-10
%R 10.1109/ICSIPA.2015.7412154
%T Markov chain Monte Carlo (MCMC) method for parameter estimation of nonlinear dynamical systems
%U https://khub.utp.edu.my/scholars/7183/
%X 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.
%Z 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