Recursive subspace identification of Hammerstein models using LS-SVM
作者： Guillaume Mercère , Marco Lovera , Stephane Lecoeuche , Laurent Bako
DOI:
关键词: Support vector machine 、 Markov chain 、 Realization (systems) 、 Least squares 、 Algorithm 、 Subspace topology 、 MIMO 、 State space 、 Computer science 、 Noise
摘要: This paper presents a recursive scheme for the identification of Hammerstein MIMO models. The Markov parameters system are determined first by Least Squares Support Vector Machines (LS-SVM) regression through an over-parameterization technique. Then, state space realization is retrieved using adapted online subspace method. Simulation results provided to demonstrate effectiveness algorithm in presence white output noise. Copyright c © 2007 IFAC
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