Title :
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
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;
Conference_Titel :
Communications Control and Signal Processing (ISCCSP), 2012 5th International Symposium on
Conference_Location :
Rome
Print_ISBN :
978-1-4673-0274-6
DOI :
10.1109/ISCCSP.2012.6217789
