Lookup NU author(s): Dr Mohsen Naqvi,
Professor Jonathon Chambers
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
In the paper, a robust and efficient system identification method is proposed for a state-space model with heavy-tailed process and measurement noises by using the maximum likelihood criterion. An expectation maximization algorithm for a state-space model with heavy-tailed process and measurement noises is derived by treating auxiliary random variables as missing data, based on which a new nonlinear system identification method is proposed. Noise parameter estimations are updated analytically and model parameter estimations are updated approximately based on the Newton method. The effectiveness of the proposed method is illustrated in a numerical example concerning a univariate non-stationary growth model.
Author(s): Huang YL, Zhang YG, Li N, Naqvi SM, Chambers J
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 19th International Conference on Information Fusion (FUSION)
Year of Conference: 2016
Online publication date: 04/08/2016
Acceptance date: 01/01/1900
Publisher: Institute of Electrical and Electronics Engineers
Library holdings: Search Newcastle University Library for this item
Series Title: Information Fusion (FUSION), 2016 19th International Conference on