- Tytuł:
- Nonlinear system identification with a real-coded genetic algorithm (RCGA)
- Autorzy:
-
Cherif, I.
Fnaiech, F. - Powiązania:
- https://bibliotekanauki.pl/articles/329753.pdf
- Data publikacji:
- 2015
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
blind nonlinear identification
Volterra series
higher order cumulants
real-coded genetic algorithm
szereg Volterry
kumulanta wyższego rzędu
algorytm genetyczny kodowania rzeczywistego - Opis:
- 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.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2015, 25, 4; 863-875
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł