On the identification of model error through observations of time-varying parameters

Green, PL, Chodora, E and Atamturktur, S (2018) On the identification of model error through observations of time-varying parameters. In: ISMA-USD Conference 2018.

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Abstract

When performing system identification, it can be possible to realise a deficient model (i.e. one that will make low fidelity predictions) that is able to closely represent a set of training data. For example, the parameters of linear dynamical models can often be tuned to realise a close match to training data that was generated from a system with strong nonlinearities. Despite this close match to available data, these same models may make very poor-quality predictions when shifted even slightly from the 'validation domain' (which could, for example, be a specific time window). In this paper we investigate the hypothesis that, by treating our model's parameters as being time-varying, we can identify key weaknesses in a model that would have been difficult to establish using other identification methods that do not consider the potentially time-varying nature of the model's parameters. Specifically, we use an Extended Kalman Filter to 'track' the parameters of a dynamical system, as a time history of training data is analysed. We then illustrate that this approach can reveal important information about the potential deficiencies of a model.

Item Type:	Conference or Workshop Item (Unspecified)
Depositing User:	Symplectic Admin
Date Deposited:	08 Jun 2018 14:55
Last Modified:	26 Apr 2024 08:55
Related URLs:	Author
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3022213