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A Bayesian parameter learning procedure for nonlinear dynamical systems via the ensemble Kalman filter

Ur Rehman, M.J. and Dass, S.C. and Asirvadam, V.S. (2018) A Bayesian parameter learning procedure for nonlinear dynamical systems via the ensemble Kalman filter. Proceedings - 2018 IEEE 14th International Colloquium on Signal Processing and its Application, CSPA 2018 . pp. 161-166.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Dynamical systems are a natural and convenient way to model the evolution of processes observed in practice. When uncertainty is considered and incorporated, these system become known as stochastic dynamical systems. Based on observations made from stochastic dynamical systems, we consider the issue of parameter learning, and a related state estimation problem. We develop a Markov Chain Monte Carlo (MCMC) algorithm, which is an iterative method, for parameter inference. Within the parameter learning steps, the MCMC algorithm requires to perform state estimation for which the target distribution is constructed by using the Ensemble Kalman filter (EnKF). The methodology is illustrated using two examples of nonlinear stochastic dynamical systems. © 2018 IEEE.

Item Type:Article
Impact Factor:cited By 0; Conference of 14th IEEE International Colloquium on Signal Processing and its Application, CSPA 2018 ; Conference Date: 9 March 2018 Through 10 March 2018; Conference Code:136804
Uncontrolled Keywords:Bandpass filters; Dynamical systems; Inference engines; Iterative methods; Kalman filters; Markov processes; Monte Carlo methods; Nonlinear dynamical systems; State estimation; Stochastic systems, Bayesian; Ensemble Kalman Filter; Estimation problem; Markov chain monte carlo algorithms; MCMC algorithms; Parameter inference; Parameter learning; Stochastic dynamical system, Parameter estimation
Departments / MOR / COE:Research Institutes > Institute for Autonomous Systems
ID Code:21559
Deposited By: Ahmad Suhairi
Deposited On:25 Sep 2018 06:23
Last Modified:09 Nov 2018 06:18

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