Recursive Identification of Hammerstein Systems Using Polynomial Function Approximate

Nonlinear system identification of Hammerstein model was studied.The nonlinear static function was approximated by a number of polynomial functions.The basic method is to replace immeasurable noise terms in the information vectors by their estimates,and to compute the noise estimates based on the obtained parameter estimates.It is based on a piecewise-linear Hammerstein model,which is linear in the parameters.The identification procedure is divided into two steps.In step 1,the extended stochastic gradient algorithm is adopted to identify some unknown parameters.In step 2,based on signal value decomposition(SVD),this paper proposes a new method to identify the other parameters.Simulations results demonstrate the validity of the proposed approach.