<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Nonlinear System Identification with a Real–Coded Genetic Algorithm (RCGA)

Laboratory of Signal, Image and Energy Mastery (SIME), National Higher Engineering School of Tunis (ENSIT), 5 Av. Taha Hussein, 1008, Tunis, Tunisia

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Nonlinear System Identification with a Real\u2013Coded Genetic Algorithm (RCGA)"}

This paper is devoted to the blind identification problem of a special class of nonlinear systems, namely, Volterra models, using a real-coded genetic...

Nonlinear System Identification with a Real–Coded Genetic Algorithm (RCGA)

Data publikacji: 30 gru 2015

Zakres stron: 863 - 875

Otrzymano: 20 lut 2014

DOI: https://doi.org/10.1515/amcs-2015-0062

Słowa kluczowe
blind nonlinear identification, Volterra series, higher order cumulants, real-coded genetic algorithm

© 2015 Imen Cherif et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Nonlinear System Identification with a Real–Coded Genetic Algorithm (RCGA)

Data publikacji: 30 gru 2015

Zakres stron: 863 - 875

Otrzymano: 20 lut 2014

DOI: https://doi.org/10.1515/amcs-2015-0062

Słowa kluczoweblind nonlinear identification, Volterra series, higher order cumulants, real-coded genetic algorithm

© 2015 Imen Cherif et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
blind nonlinear identification, Volterra series, higher order cumulants, real-coded genetic algorithm