Title :
On semiparametric identification of MISO Hammerstein systems
Author :
Pawlak, Miroslaw ; Lv, Jiaqing
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Manitoba, Winnipeg, MB, Canada
Abstract :
In this paper we develop a semi-parametric approach to the problem of identification of multivariate Hammerstein systems. A nonlinearity in general multivariate Hammerstein systems is represented by projecting the d-dimensional input signal onto one dimensional subset which, in turn, is mapped by a univariate nonparametric function to an internal signal of the system. Such a parsimonious representation allows us to overcome the curse of dimensionality present in the multivariate Hammerstein system. We identify the Hammerstein system via the semi-parametric version of the least- squares. A discussion on the statistical accuracy of the resulting estimates is given. This is also verified in numerous simulation studies.
Keywords :
least squares approximations; multivariable systems; nonlinear systems; parameter estimation; set theory; signal processing; MISO Hammerstein system; d-dimensional input signal; least-squares method; multiple-input single-output Hammerstein system; multivariate Hammerstein system; nonlinear system; one dimensional subset; semiparametric identification; univariate nonparametric function; Accuracy; Approximation methods; Convergence; Kernel; Nonlinear dynamical systems; Training; Training data; MISO Hammerstein systems; consistency; curse of dimensionality; linear projection; semi-parametric inference;
Conference_Titel :
Digital Signal Processing Workshop and IEEE Signal Processing Education Workshop (DSP/SPE), 2011 IEEE
Conference_Location :
Sedona, AZ
Print_ISBN :
978-1-61284-226-4
DOI :
10.1109/DSP-SPE.2011.5739243
