Basis function networks for interpolation of local linear models

In this paper, a new algorithm (LOLIMOT) for nonlinear dynamic system identification with local linear models is proposed. The input space is partitioned by a tree-construction algorithm. The local models are interpolated by overlapping local basis functions. The resulting structure is equivalent to...

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Bibliographic Details
Published inProceedings of 35th IEEE Conference on Decision and Control Vol. 1; pp. 470 - 475 vol.1
Main Authors Nelles, O., Isermann, R.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1996
Subjects
Automatic control
Automation
Control engineering
Feedback
Interpolation
Laboratories
Nonlinear dynamical systems
Partitioning algorithms
Predictive models
System identification
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Summary:In this paper, a new algorithm (LOLIMOT) for nonlinear dynamic system identification with local linear models is proposed. The input space is partitioned by a tree-construction algorithm. The local models are interpolated by overlapping local basis functions. The resulting structure is equivalent to a Sugeno-Takagi fuzzy system and a local model network and can therefore be interpreted correspondingly. The LOLIMOT algorithm is very simple, easy to implement, and fast. Moreover, this approach has the following appealing properties: it does not underlie the "curse of dimensionality", it reveals irrelevant inputs, it detects inputs that influence the output mainly in a linear way, and it applies robust local linear estimation schemes. The drawbacks are that only orthogonal cuts are performed and that the local estimation approach may lead to interpolation errors.
ISBN:9780780335905
0780335902
ISSN:0191-2216
DOI:10.1109/CDC.1996.574356