Computer Standards & Interfaces - Special issue on Analog-to-digital converters (ADCs) testing
Blind identification of second order Hammerstein series
Signal Processing
Diagonal Kernel point estimation of nth-order discrete Volterra-Wiener systems
EURASIP Journal on Applied Signal Processing
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Modeling and identification of nonlinear systems in the short-time fourier transform domain
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
A cumulant based algorithm for the identification of input-output quadratic systems
Automatica (Journal of IFAC)
Identification of discrete-time state affine state space models using cumulants
Automatica (Journal of IFAC)
| Hi-index | 35.69 |
The paper is concerned with the identification of nonlinear systems represented 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 time as well as in the frequency domain are developed based on cross-cumulant information. The derived kernels are optimal in the mean squared error sense for noncausal systems. Order recursive procedures based on minimum mean squared error reduction are derived. More general input output representations that result when the Volterra kernels are expanded in a given orthogonal base are also considered