Accessibility navigation

Genetic least squares for system identification

Warwick, K., Kang, Y. -H. and Mitchell, R. J. (1999) Genetic least squares for system identification. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 3 (4). pp. 200-205. ISSN 1432-7643

Full text not archived in this repository.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1007/s005000050070


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.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:17800

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation