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Soft Computing

, Volume 3, Issue 4, pp 200–205 | Cite as

Genetic least squares for system identification

  • K. Warwick
  • Y. -H. Kang
  • R. J. Mitchell
Original paper

Abstract

The recursive least-squares algorithm with a forgetting factor has been extensively applied and studied for the on-line parameter estimation of linear dynamic systems. This paper explores the use of genetic algorithms to improve the performance of the recursive least-squares algorithm in the parameter estimation of time-varying systems. Simulation results show that the hybrid recursive algorithm (GARLS), combining recursive least-squares with genetic algorithms, can achieve better results than the standard recursive least-squares algorithm using only a forgetting factor.

Keywords

Dynamic System Genetic Algorithm Parameter Estimation System Identification Recursive Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • K. Warwick
    • 1
  • Y. -H. Kang
    • 1
  • R. J. Mitchell
    • 1
  1. 1.Department of Cybernetics, University of Reading, Reading, RG6 6AY, UKGB

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