Title :
Wiener-Hammerstein systems modeling using diagonal Volterra kernels coefficients
Author :
Kibangou, Alain Y. ; Favier, Gérard
Author_Institution :
Lab. I3S, CNRS-UNSA, Sophia Antipolis, France
fDate :
6/1/2006 12:00:00 AM
Abstract :
In this letter, we first present explicit relations between block-oriented nonlinear representations and Volterra models. For an identification purpose, we show that the estimation of the diagonal coefficients of the Volterra kernels associated with the considered block-oriented nonlinear structures is sufficient to recover the overall model. An alternating least squares-type algorithm is provided to carry out this model identification.
Keywords :
identification; least squares approximations; nonlinear systems; stochastic processes; Wiener-Hammerstein system; block-oriented nonlinear representation; diagonal Volterra kernels coefficient; least squares-type algorithm; parameter estimation; Digital communication; Kernel; Least squares approximation; Nonlinear filters; Nonlinear systems; Parameter estimation; Polynomials; Power system modeling; Radiofrequency amplifiers; Signal processing algorithms; Alternating least squares (ALS); Toeplitz matrix; Volterra models; Wiener–Hammerstein models; bilinear decomposition; parameter estimation;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2006.871705
