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

Chen, H., Gong, Y. and Hong, X. ORCID: https://orcid.org/0000-0002-6832-2298 (2016) A new adaptive multiple modelling approach for non-linear and non-stationary systems. International Journal of Systems Science, 47 (9). pp. 2100-2110. ISSN 0020-7721 doi: 10.1080/00207721.2014.973926

Abstract/Summary

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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/65636
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
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