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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/25657

Title: A new adaptive multiple modelling approach for non-linear and non-stationary systems
Authors: Chen, Hao
Gong, Yu
Hong, Xia
Keywords: Online modelling
Multiple model
Time series
Issue Date: 2014
Publisher: © Taylor & Francis
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.
Abstract: 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.
Description: 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.
Sponsor: This research is sponsored by the UK Engineering and Physical Sciences Research Council and DSTL under the grant number EP/H012516/1.
Version: Accepted for publication
DOI: 10.1080/00207721.2014.973926
URI: https://dspace.lboro.ac.uk/2134/25657
Publisher Link: http://dx.doi.org/10.1080/00207721.2014.973926
ISSN: 0020-7721
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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