Blind Nonlinear System Identification Under Gaussian And / Or I.I.D. Excitation Using Genetic Algorithms

Author

Cherif, I. ; Abid, S. ; Fnaiech, Farhat

Author_Institution

Signal, Image et Commande Intelligente des Syst. Industriels, ESSTT, Tunis, Tunisia

fYear

2007

fDate

24-27 Nov. 2007

Firstpage

644

Lastpage

647

Abstract

The blind identification of a special class of nonlinear system is pursued in this paper. In particular a genetic algorithm is developed for blind identification of quadratic Volterra model excited by an unobservable input signal which can either be a stationary Gaussian process or an i.i.d process. This approach enables a nonlinear relationship between model kernels and output cumulants up to third order. Simulation results are presented to show good performance of this approach.

Keywords

Volterra series; genetic algorithms; nonlinear systems; signal processing; blind nonlinear system identification; excitation; genetic algorithms; model kernels; output cumulants; quadratic Volterra model; stationary Gaussian process; unobservable input signal; Gaussian processes; Genetic algorithms; Hydrogen; Intelligent systems; Kernel; Nonlinear equations; Nonlinear systems; Signal processing; Signal processing algorithms; Statistical analysis; Blind Identification; Genetic Algorithm (GA); Volterra kernels; higher order cumulants;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Communications, 2007. ICSPC 2007. IEEE International Conference on

Conference_Location

Dubai

Print_ISBN

978-1-4244-1235-8

Electronic_ISBN

978-1-4244-1236-5

Type

conf

DOI

10.1109/ICSPC.2007.4728401

Filename

4728401