A new ARIV identification algorithm for stochastic reduced complexty Volterra model

Author

Laamiri, Imen ; Khouaja, Anis ; Messaoud, Hassani

Author_Institution

Electr. Eng. Dept., ENIM, Monastir, Tunisia

fYear

2013

fDate

6-8 May 2013

Firstpage

470

Lastpage

475

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, results from tensor decomposition of kernels of classical Volterra model. The proposed algorithm uses the Recursive Instrumental Variable (RIV) method in alternating way to estimate the model parameters of the model. The algorithm validation is ensured by simulation results.

Keywords

Volterra series; computational complexity; nonlinear systems; parameter estimation; singular value decomposition; stochastic systems; tensors; ARIV identification algorithm; SVD-PARAFAC-Volterra model; alternating recursive instrumental variable; classical Volterra model kernel tensor decomposition; model parameter estimation; nonlinear stochastic system; stochastic identification algorithm; stochastic reduced complexity Volterra model; Instruments; Mathematical model; Matrix decomposition; Stochastic processes; Tensile stress; Vectors; Writing; PARAFAC; RIV; Volterra model; identification; non-linear stochastic system;

fLanguage

English

Publisher

ieee

Conference_Titel

Control, Decision and Information Technologies (CoDIT), 2013 International Conference on

Conference_Location

Hammamet

Print_ISBN

978-1-4673-5547-6

Type

conf

DOI

10.1109/CoDIT.2013.6689590

Filename

6689590