By Topic

Tensor Analysis-Based Model Structure Determination and Parameter Estimation for Block-Oriented Nonlinear Systems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Kibangou, A.Y. ; Syst. Control Dept., Univ. Joseph Fourier, St. Martin d''Heres, France ; Favier, G.

A block-oriented nonlinear system is composed of a concatenation of blocks representing either memoryless nonlinearities or linear dynamic subsystems. Wiener, Hammerstein, and Wiener-Hammerstein (WH) models are the most commonly used ones for modeling such block-oriented nonlinear systems. In this paper, we develop a tensor analysis-based approach for determining both the structure and the parameters of the most appropriate model among the three above listed models. The structure is deduced from the rank of a tensor obtained by convolving a random finite impulse response (FIR) linear filter with a p th-order Volterra kernel (p > 2), associated with the block-oriented nonlinear system to be identified. The parameters of the linear subsystems are obtained from the PARAllel FACtor analysis (PARAFAC) decomposition of the pth-order Volterra kernel associated with the original nonlinear system and/or an extended WH system, whereas those of the nonlinear subsystem are estimated using the least squares method. The performance of the proposed identification scheme is illustrated by means of some simulation results.

Published in:

Selected Topics in Signal Processing, IEEE Journal of  (Volume:4 ,  Issue: 3 )