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

• Autorzy: Cherif I. , Fnaiech F.

Identyfikatory

DOI

10.1515/amcs-2015-0062

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

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

PL

szereg Volterry; kumulanta wyższego rzędu; algorytm genetyczny kodowania rzeczywistego

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2015
• Tom: Vol. 25, no. 4
• Strony: 863--875
• Opis fizyczny: Bibliogr. 25 poz., rys., tab., wykr.

Twórcy

autor

Cherif I.

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

autor

Fnaiech F.

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

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