Online kernel adaptive algorithms with dictionary adaptation for MIMO models
Résumé
Nonlinear system identification has always been a challenging problem. The use of kernel methods to solve such problems becomes more prevalent. However, the complexity of these methods increases with time which makes them unsuitable for online identification. This drawback can be solved with the introduction of the coherence criterion. Furthermore, dictionary adaptation using a stochastic gradient method proved its efficiency. Mostly, all approaches are used to identify Single Output models which form a particular case of real problems. In this letter we investigate online kernel adaptive algorithms to identify Multiple Inputs Multiple Outputs model as well as the possibility of dictionary adaptation for such models.
Mots clés
sparsity
adaptive filtering
MIMO communication
nonlinear systems
operating system kernels
online kernel adaptive algorithms
dictionary adaptation
MIMO models
nonlinear system identification
online identification
coherence criterion
stochastic gradient method
single output models
multiple inputs multiple outputs model
Dictionaries
Kernel
Adaptation models
MIMO
Coherence
Signal processing algorithms
Kernel methods
machine learning
nonlinear adaptive filters
| Origine | Fichiers produits par l'(les) auteur(s) |
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