Identification of Third-Order Volterra-PARAFAC Models Based on PARAFAC Decomposition Using a Tensor Approach

Authors: ,

Abstract: Volterra models are very useful for representing nonlinear systems with vanishing memory. The main drawback of these models is their huge number of parameters to be estimated. In this paper, we present a new class of Volterra models, called Volterra-Parafac models, with a reduced parametric complexity, by considering Volterra kernels of order (p > 2) as symmetric tensors and by using a parallel factor (PARAFAC) decomposition. This paper is concerned with the problem of identification of third-order Volterra-PARAFAC models. Two types of algorithms are proposed for estimating the parameters of these models when input-output signals and kernel coefficients are real valued. The first is called Levenberg-Marquardt algorithm and the second is the Partial Update LMS algorithms. Some simulation results illustrate the proposed identification methods.

WSEAS Transactions on Signal Processing, ISSN / E-ISSN: 1790-5052 / 2224-3488, Volume 13, 2017, Art. #25

PDF
XML
Cite
×
Citation Tools
WSEAS AMA
Zouhour Ben Ahmed, Nabil Derbel, "Identification of Third-Order Volterra-PARAFAC Models Based on PARAFAC Decomposition Using a Tensor Approach," WSEAS Transactions on Signal Processing, vol. 13, pp. 223-231, 2017, DOI:
Zouhour Ben Ahmed, Nabil Derbel. Identification of Third-Order Volterra-PARAFAC Models Based on PARAFAC Decomposition Using a Tensor Approach. WSEAS Transactions on Signal Processing. 2017;13:223-231.
Copy to Clipboard
Citation copied to Clipboard

Certification

---