Identification of nonlinear stochastic systems described by a reduced complexity Volterra model using an ARGLS algorithm

Author

Laamiri, Imen ; Khouaja, I. Laamiri A ; Messaoud, H.

Author_Institution

Unite de Rech. Autom., Traitement du Signal et de l´´Image (ATSI), ENIM, Monastir, Tunisia

fYear

2012

fDate

2-4 May 2012

Firstpage

1

Lastpage

5

Abstract

This paper proposes a stochastic identification algorithm of a model describing non linear stochastic system. The identified model known as SVD-PARAFAC-Volterra model [1] results from tensor decomposition of kernels of classical Volterra model. The proposed algorithm uses the Recursive Generalized Least Square (RGLS) method in alternative way to estimate the parameters of the model. The algorithm validation is ensured by simulation results.

Keywords

Volterra series; least squares approximations; nonlinear systems; recursive estimation; singular value decomposition; statistical analysis; stochastic systems; ARGLS algorithm; SVD-PARAFAC-Volterra model; Volterra model kernels; alternating recursive generalized least square method; nonlinear stochastic system identification; reduced complexity Volterra model; stochastic identification algorithm; tensor decomposition; Complexity theory; Kernel; Matrix decomposition; Signal processing algorithms; Tensile stress; Vectors; Writing; Identification; PARAFAC; RGLS; Stochastic system; Volterra kernels; Volterra model;

fLanguage

English

Publisher

ieee

Conference_Titel

Communications Control and Signal Processing (ISCCSP), 2012 5th International Symposium on

Conference_Location

Rome

Print_ISBN

978-1-4673-0274-6

Type

conf

DOI

10.1109/ISCCSP.2012.6217789

Filename

6217789