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A robust and efficient system identification method for a state-space model with heavy-tailed process and measurement noises

Lookup NU author(s): Dr Mohsen Naqvi, Professor Jonathon Chambers

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Abstract

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.


Publication metadata

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

Pages: 441-448

Online publication date: 04/08/2016

Acceptance date: 01/01/1900

Publisher: Institute of Electrical and Electronics Engineers

URL: http://ieeexplore.ieee.org/document/7527922/

Library holdings: Search Newcastle University Library for this item

Series Title: Information Fusion (FUSION), 2016 19th International Conference on

ISBN: 9780996452748


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