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Recursive subspace identification of Hammerstein models based on least squares support vector machines

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4 Author(s)
Bako, L. ; Dept. Inf. et Autom., Ecole des Mines de Douai, Douai, France ; Mercere, G. ; Lecoeuche, S. ; Lovera, M.

A recursive scheme for the identification of SIMO Hammerstein models is presented. In the proposed scheme, first the Markov parameters of the system are determined, by a least squares support vector machines regression through an over-parameterisation technique. Then, a state-space realisation of the system is retrieved using a recursive subspace identification method. Simulation results are provided to demonstrate the effectiveness of the algorithm.

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Control Theory & Applications, IET  (Volume:3 ,  Issue: 9 )