Combining evolutionary and stochastic gradient techniques for system identification

DOI10.1016/J.CAM.2008.07.014zbMath1161.65052OpenAlexW2028483073MaRDI QIDQ1012510

Konstantinos Theofilatos, Grigorios N. Beligiannis, Spiridon D. Likothanassis

Publication date: 21 April 2009

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2008.07.014

zbMATH Keywords

system identificationmodelingnumerical examplesgenetic algorithms (GAs)hybrid evolutionary algorithmsautoregressive moving average ARMAleast mean squares (LMS)stochastic gradient techniques

Mathematics Subject Classification ID

Numerical optimization and variational techniques (65K10) Stochastic programming (90C15) System identification (93B30)

Related Items (1)

A novel APSO-aided maximum likelihood identification method for Hammerstein systems

Uses Software

• GALib
• Genocop

Cites Work

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• Hybrid evolutionary algorithms.
• Vector ARMA estimation: a reliable subspace approach
• Unbiased recursive identification using model reference adaptive techniques
• Least Mean Square Adaptive Digital Background Calibration of Pipelined Analog-to-Digital Converters

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