Identification of MIMO Hammerstein models using least squares support vector machines. (English) Zbl 1086.93064

Goethals, Ivan; Pelckmans, Kristiaan; Suykens, Johan A. K.; De Moor, Bart

Automatica 41, No. 7, 1263-1272 (2005).

The paper deals with the identification task of SISO and MIMO Hammerstein systems. Linear in the parameters (affine) and ARX model structure corrupted by random disturbances is assumed for the static nonlinearity and linear system dynamics, respectively, and then least squares support vector machines framework is used for determining the unknown nonlinearity and dynamical part parameters from input-output data. Proper computational routines are presented and efficiency of the approach is illustrated by means of simulation examples.

Reviewer: Zygmunt Hasiewicz (Wrocław)

MSC:

93E12	Identification in stochastic control theory
93E25	Computational methods in stochastic control (MSC2010)
62F10	Point estimation

Keywords:

Hammerstein system; support vector machines; system identification; kernel methods; ARX; LS-SVM; MIMO

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