Online kernel adaptive algorithms with dictionary adaptation for MIMO models

Chafic Saidé (1) , Régis Lengellé (1) , Paul Honeine (1) , Roger Achkar (2)

1 LM2S - Laboratoire Modélisation et Sûreté des Systèmes
2 AUST - American University of Science and Technology

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

Domaines: Apprentissage [cs.LG]; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Chafic Saidé, Régis Lengellé, Paul Honeine, Roger Achkar. Online kernel adaptive algorithms with dictionary adaptation for MIMO models. IEEE Signal Processing Letters, 2013, 20 (5), pp.535 - 538. ⟨10.1109/LSP.2013.2254711⟩. ⟨hal-01965578⟩