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

Abstract

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.