NONLINEAR-SYSTEM IDENTIFICATION USING GAUSSIAN INPUTS

This paper is concerned with the identification of nonlinear systems r epresented by Volterra expansions and driven by stationary, zero mean Gaussian inputs, with arbitrary spectra that are not necessarily white , Procedures for the computation of the Volterra kernels both in the t ime as well as in the frequency domain are developed based on crosscum ulant information, The derived kernels are optimal in the mean squared error sense for noncausal systems, Order recursive procedures based o n minimum mean squared error reduction are derived, More general input output representations that result when the Volterra kernels are expa nded in a given orthogonal base are also considered.