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• W2295996871 abstract "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." @default.
• W2295996871 created "2016-06-24" @default.
• W2295996871 creator A5003440018 @default.
• W2295996871 creator A5070554293 @default.
• W2295996871 date "2015-12-01" @default.
• W2295996871 modified "2023-09-23" @default.
• W2295996871 title "Nonlinear System Identification with a Real–Coded Genetic Algorithm (RCGA)" @default.
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• W2295996871 doi "https://doi.org/10.1515/amcs-2015-0062" @default.
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