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Paper details
Number 4 - December 2015
Volume 25 - 2015
Nonlinear system identification with a real-coded genetic algorithm (RCGA)
Imen Cherif, Farhat Fnaiech
Abstract
This 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.
Keywords
blind nonlinear identification, Volterra series, higher order cumulants, real-coded genetic algorithm