Paper Info
Title
Nonlinear System Identification With A Real-Coded Genetic Algorithm (RCGA).
Abstract
AbstractThis paper is devoted to the blind identification problem of a special class of nonlinear systems, namely, Volterra models, using a real-coded genetic algorithm RCGA. The model input is assumed to be a stationary Gaussian sequence or an independent identically distributed i.i.d. process. The order of the Volterra series is assumed to be known. The fitness function is defined as the difference between the calculated cumulant values and analytical equations in which the kernels and the input variances are considered. Simulation results and a comparative study for the proposed method and some existing techniques are given. They clearly show that the RCGA identification method performs better in terms of precision, time of convergence and simplicity of programming.
Year
DOI
Venue
2015
10.1515/amcs-2015-0062
Applied Mathematics and Computer Science
Keywords
Field
DocType
blind nonlinear identification, Volterra series, higher order cumulants, real-coded genetic algorithm
Convergence (routing),Mathematical optimization,Nonlinear system,Control theory,Nonlinear system identification,Volterra series,Fitness function,Gaussian,Parameter identification problem,Mathematics,Genetic algorithm
Journal
Volume
Issue
ISSN
25
4
1641-876X
Citations 
PageRank 
References 
0
0.34
11
Authors
2
Name
Order
Citations
PageRank
Imen Cherif121.08
Farhat Fnaiech220924.97