1. bookVolume 25 (2015): Issue 4 (December 2015)
    Special issue: Complex Problems in High-Performance Computing Systems, Editors: Mauro Iacono, Joanna Kołodziej
Journal Details
License
Format
Journal
eISSN
2083-8492
First Published
05 Apr 2007
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4 times per year
Languages
English
access type Open Access

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

Published Online: 30 Dec 2015
Page range: 863 - 875
Received: 20 Feb 2014
Journal Details
License
Format
Journal
eISSN
2083-8492
First Published
05 Apr 2007
Publication timeframe
4 times per year
Languages
English
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.

Keywords

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