multiplemodel.pdf (507.52 kB)
Download file

A new adaptive multiple modelling approach for non-linear and non-stationary systems

Download (507.52 kB)
journal contribution
posted on 30.06.2017, 12:19 by Hao Chen, Yu GongYu Gong, Xia Hong
This paper proposes a novel adaptive multiple modelling algorithm for non-linear and non-stationary systems. This simple modelling paradigm comprises K candidate sub-models which are all linear. With data available in an online fashion, the performance of all candidate sub-models are monitored based on the most recent data window, and M best sub-models are selected from the K candidates. The weight coefficients of the selected sub-model are adapted via the recursive least square (RLS) algorithm, while the coefficients of the remaining sub-models are unchanged. These M model predictions are then optimally combined to produce the multi-model output. We propose to minimise the mean square error based on a recent data window, and apply the sum to one constraint to the combination parameters, leading to a closed-form solution, so that maximal computational efficiency can be achieved. In addition, at each time step, the model prediction is chosen from either the resultant multiple model or the best sub-model, whichever is the best. Simulation results are given in comparison with some typical alternatives, including the linear RLS algorithm and a number of online non-linear approaches, in terms of modelling performance and time consumption.

Funding

This research is sponsored by the UK Engineering and Physical Sciences Research Council and DSTL under the grant number EP/H012516/1.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE

Volume

47

Issue

9

Pages

2100 - 2110 (11)

Citation

CHEN, H., GONG, Y. and HONG, X., 2014. A new adaptive multiple modelling approach for non-linear and non-stationary systems. International Journal of Systems Science, 47 (9), pp. 2100-2110.

Publisher

© Taylor & Francis

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

03/06/2014

Publication date

2014

Notes

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Systems Science on 31st October 2014, available online: http://www.tandfonline.com/10.1080/00207721.2014.973926.

ISSN

0020-7721

eISSN

1464-5319

Language

en