Nonlinear system identification with a real-coded genetic algorithm (RCGA) - Cherif, Imen; Fnaiech, Farhat (Zielona Góra: Uniwersytet Zielonogórski)
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.
Keywords: blind nonlinear identification; Volterra series; higher order cumulants; real-coded genetic algorithm