14th IFAC Symposium on System Identification, SYSID 2006

SYSID-2006 Paper Abstract


Paper FrA1.5

Schön, Thomas B. (Linköping Univ.), Wills, Adrian George (Univ. of Newcastle), Ninness, Brett (Univ. of Newcastle)

Maximum Likelihood Nonlinear System Estimation

Scheduled for presentation during the Regular Session "Identification and Filtering of Nonlinear Systems" (FrA1), Friday, March 31, 2006, 11:50−12:10, Concert Hall

14th IFAC Symposium on System Identification, March 29 - 31, 2006, Newcastle, Australia

This information is tentative and subject to change. Compiled on July 17, 2018

Keywords Maximum Likelihood Methods, Particle Filtering/Monte Carlo Methods, Nonlinear System Identification


This paper is concerned with the parameter estimation of a relatively general class of nonlinear dynamic systems. A Maximum Likelihood (ML) framework is employed in the interests of statistical efficiency, and it is illustrated how an Expectation Maximisation (EM) algorithm may be used to compute these ML estimates. An essential ingredient is the employment of so-called ``particle smoothing'' methods to compute required conditional expectations via a Monte Carlo approach. A simulation example demonstrates the efficacy of these techniques.