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

Imen Cherif; Farhat Fnaiech

International Journal of Applied Mathematics and Computer Science (2015)

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

Abstract

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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.

How to cite

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Imen 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 -

References

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