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 doi: 10.1007/s005000050070

Abstract/Summary

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
URI	https://reading-clone.eprints-hosting.org/id/eprint/17800
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Publisher	Springer
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