TY  - CONF
ID  - scholars7183
TI  - Markov chain Monte Carlo (MCMC) method for parameter estimation of nonlinear dynamical systems
SP  - 7
KW  - 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
KW  -  Bayesian; Bayesian frameworks; Markov chain Monte Carlo method; Numerical integrations; Parameter; Performance comparison; Stochastic differential equations; Taylor series approximation
KW  -  Parameter estimation
N1  - 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
N2  - 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.
AV  - none
EP  - 10
A1  - Ur Rehman, M.J.
A1  - Dass, S.C.
A1  - Asirvadam, V.S.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971644702&doi=10.1109%2fICSIPA.2015.7412154&partnerID=40&md5=5fbb87ef138e915bb6c4253b8846dc88
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9781479989966
Y1  - 2016///
ER  -