# Nonlinear system identification with a real-coded genetic algorithm (RCGA)

International Journal of Applied Mathematics and Computer Science (2015)

- Volume: 25, Issue: 4, page 863-875
- ISSN: 1641-876X

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topImen Cherif, and Farhat Fnaiech. "Nonlinear system identification with a real-coded genetic algorithm (RCGA)." International Journal of Applied Mathematics and Computer Science 25.4 (2015): 863-875. <http://eudml.org/doc/275967>.

@article{ImenCherif2015,

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.},

author = {Imen Cherif, Farhat Fnaiech},

journal = {International Journal of Applied Mathematics and Computer Science},

keywords = {blind nonlinear identification; Volterra series; higher order cumulants; real-coded genetic algorithm},

language = {eng},

number = {4},

pages = {863-875},

title = {Nonlinear system identification with a real-coded genetic algorithm (RCGA)},

url = {http://eudml.org/doc/275967},

volume = {25},

year = {2015},

}

TY - JOUR

AU - Imen Cherif

AU - Farhat Fnaiech

TI - Nonlinear system identification with a real-coded genetic algorithm (RCGA)

JO - International Journal of Applied Mathematics and Computer Science

PY - 2015

VL - 25

IS - 4

SP - 863

EP - 875

AB - 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.

LA - eng

KW - blind nonlinear identification; Volterra series; higher order cumulants; real-coded genetic algorithm

UR - http://eudml.org/doc/275967

ER -

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