InforLorV4, Main, Merge, bibRecord, 002963

Nonlinear Hybrid System Identification with Kernel Models

Identifieur interne : 002963 ( Main/Merge ); précédent : 002962; suivant : 002964

Nonlinear Hybrid System Identification with Kernel Models

Auteurs : Fabien Lauer [France] ; Gérard Bloch [France] ; René Vidal [États-Unis]

Source :

RBID : Hal:hal-00514429

English descriptors

mix :
- Kernel Models, Nonlinear Hybrid System Identification, continuous optimization, fixed-size Least Squares SVM (LS-SVM), large data sets, support vector selection.

Abstract

This paper focuses on the identification of nonlinear hybrid systems involving unknown nonlinear dynamics. The proposed method extends the framework of [1] by introducing nonparametric models based on kernel functions in order to estimate arbitrary nonlinearities without prior knowledge. In comparison to the previous work of [2], which also dealt with unknown nonlinearities, the new algorithm assumes the form of an unconstrained nonlinear continuous optimization problem, which can be efficiently solved for moderate numbers of parameters in the model, as is typically the case for linear hybrid systems. However, to maintain the efficiency of the method on large data sets with nonlinear kernel models, a preprocessing step is required in order to fix the model size and limit the number of optimization variables. A support vector selection procedure, based on a maximum entropy criterion, is proposed to perform this step. The efficiency of the resulting algorithm is demonstrated on large-scale experiments involving the identification of nonlinear switched dynamical systems.

Url:

https://hal.archives-ouvertes.fr/hal-00514429

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to stream Hal, to step Curation: 003612
to stream Hal, to step Checkpoint: 002134

Links to Exploration step

Hal:hal-00514429

Le document en format XML

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<front><div type="abstract" xml:lang="en">This paper focuses on the identification of nonlinear hybrid systems involving unknown nonlinear dynamics. The proposed method extends the framework of [1] by introducing nonparametric models based on kernel functions in order to estimate arbitrary nonlinearities without prior knowledge. In comparison to the previous work of [2], which also dealt with unknown nonlinearities, the new algorithm assumes the form of an unconstrained nonlinear continuous optimization problem, which can be efficiently solved for moderate numbers of parameters in the model, as is typically the case for linear hybrid systems. However, to maintain the efficiency of the method on large data sets with nonlinear kernel models, a preprocessing step is required in order to fix the model size and limit the number of optimization variables. A support vector selection procedure, based on a maximum entropy criterion, is proposed to perform this step. The efficiency of the resulting algorithm is demonstrated on large-scale experiments involving the identification of nonlinear switched dynamical systems.</div>
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Nonlinear Hybrid System Identification with Kernel Models

Nonlinear Hybrid System Identification with Kernel Models

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Abstract

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