Title :
Parametric Identification of Hammerstein Systems With Consistency Results Using Stochastic Inputs
Author_Institution :
Dept. of Autom., Tsinghua Univ., Beijing, China
Abstract :
The extended least squares algorithm is applied to identify the Hammerstein system, where the nonlinear static function f(??) is expressed as a linear combination of basic functions with unknown coefficients. Strong consistency of the estimates is established and their convergent rates are obtained as well.
Keywords :
convergence; identification; least squares approximations; stochastic processes; Hammerstein systems; basic functions; consistency results; convergent rates; extended least squares algorithm; linear combination; nonlinear static function; parametric identification; stochastic inputs; strong consistency; unknown coefficients; Algorithm design and analysis; Approximation algorithms; Biological system modeling; Brain modeling; Finite impulse response filter; Least squares methods; Power engineering and energy; Power system modeling; Recursive estimation; Stochastic systems; ARMAX; Hammerstein system; extended least squares (ELS); strong consistency;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.2009.2036380
