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

DOI10.1515/amcs-2015-0062zbMath1347.93095OpenAlexW2295996871MaRDI QIDQ327027

Imen Cherif, Farhat Fnaiech

Publication date: 13 October 2016

Published in: International Journal of Applied Mathematics and Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1515/amcs-2015-0062

zbMATH Keywords

Volterra seriesblind nonlinear identificationhigher order cumulantsreal-coded genetic algorithm

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59) System identification (93B30) Nonlinear systems in control theory (93C10)

Related Items (1)

Test for high dimensional covariance matrices

Cites Work

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• Tackling real-coded genetic algorithms: operators and tools for behavioural analysis
• Nonlinear blind identification with three-dimensional tensor analysis
• Efficient algorithms for Volterra system identification
• Second-order Volterra system identification
• Nonlinear system identification using heterogeneous multiple models
• Instrumental-variable methods for identification of Hammerstein systems
• Lagrange programming neural networks
• Blind identification of Volterra-Hammerstein systems

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