Title :
On nonparametric identification of cascade nonlinear systems
Author :
Pawlak, M. ; Greblicki, W.
Author_Institution :
Dept. of Electr. & Comput. Eng., Manitoba Univ., Winnipeg, Man., Canada
Abstract :
Systems consisting of linear dynamic and memoryless nonlinear elements are identified. This includes the Hammerstein and Wiener cascade models. The input signal is the Gaussian stochastic process. The goal is to recover the system nonlinearity from input-output observations, signals interconnecting the elements are not measured. This is resolved by relating the posed identification problem to the theory of linear integral equations. Deconvolution procedures for recovering the nonlinearities are proposed using orthogonal series techniques. Conditions for global convergence of the established estimates are provided. The rate of convergence is also evaluated
Keywords :
Gaussian processes; cascade systems; convergence; deconvolution; identification; integral equations; nonlinear systems; nonparametric statistics; Gaussian stochastic process; Hammerstein cascade models; Wiener cascade models; cascade nonlinear systems; convergence rate; deconvolution procedures; global convergence conditions; input-output observations; linear dynamic elements; memoryless nonlinear elements; nonparametric identification; orthogonal series techniques; system nonlinearity recovery; Control systems; Convergence; Deconvolution; Integral equations; Lakes; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Signal processing; Zinc;
Conference_Titel :
Decision and Control, 1994., Proceedings of the 33rd IEEE Conference on
Conference_Location :
Lake Buena Vista, FL
Print_ISBN :
0-7803-1968-0
DOI :
10.1109/CDC.1994.411362