Title :
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
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;
Conference_Titel :
Control, Decision and Information Technologies (CoDIT), 2013 International Conference on
Conference_Location :
Hammamet
Print_ISBN :
978-1-4673-5547-6
DOI :
10.1109/CoDIT.2013.6689590
