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On the interpretation of local models in blended multiple model structures

Shorten, Robert and Murray-Smith, Roderick and Bjørgan, Roger and Gollee, Henrik (1999) On the interpretation of local models in blended multiple model structures. International Journal of Control, 72 (7-8). pp. 620-628. ISSN 1366-5820

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Abstract

The construction of non-linear dynamics by means of interpolating the behaviour of locally valid models offers an attractive and intuitively pleasing method of modelling non-linear systems. The approach is used in fuzzy logic modelling, operating regime based models, and nonlinear statistical models. The model structure suggests that the composite local models can be used to interpret, in some appropriate manner, the overall non-linear dynamics. In this paper we demonstrate that the interpretation of these local models, in the context of multiple model structures, is not as straightforward as it might initially appear. We argue that the blended multiple model system can be interpreted in two ways – as an interpolation of linearisations, or as a full parameterisation of the system. The choice of interpretation affects experiment design, parameter identification, and model validation. We then show that, in some cases, the local models give insight into full model behaviour only in a very small region of state space. More alarmingly, we demonstrate that for off-equilibrium behaviour, subject to some approximation error, a non-unique parameterisation of the model dynamics exists. Hence, qualitative conclusions drawn from the behaviour of an identified local model, e.g. regarding stable, unstable, nodal or complex behaviour, must be treated with extreme caution. The example of muscle modelling is used to illustrate these points clearly.

Item Type: Article
Keywords: Non-linear dynamics; Locally valid models; Modelling non-linear systems; Fuzzy logic modelling; Muscle modelling; Hamilton Institute.
Subjects: Science & Engineering > Electronic Engineering
Science & Engineering > Hamilton Institute
Science & Engineering > Mathematics & Statistics
Item ID: 1887
Identification Number: 10.1080/002071799220812
Depositing User: Hamilton Editor
Date Deposited: 15 Mar 2010 13:19
Journal or Publication Title: International Journal of Control
Publisher: Taylor & Francis
Refereed: Yes
URI:

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