Identification of MIMO Hammerstein models using least squares support vector machines
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.
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