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.
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Warwick, K., Kang, YH. & Mitchell, R. Genetic least squares for system identification. Soft Computing 3, 200–205 (1999). https://doi.org/10.1007/s005000050070
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DOI: https://doi.org/10.1007/s005000050070