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Recursive subspace identification of Hammerstein models using LS-SVM

Marco Lovera, Laurent Bako, Stephane Lecoeuche, Guillaume Mercère
This article presents a recursive scheme for the identification of Hammerstein MIMO systems. The Markov parameters of the system are determined first by a Least Squares Support Vector Machines (LS-SVM) regression through an over-parameterization technique. Then, a state space realization of the system is retrieved using an adapted online subspace identification method. Simulation results are provided to demonstrate the effectiveness of the algorithm in the presence white output noise.
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