Performance comparison of genetic and tabu search algorithms for system identification

Authors:
Aytekin Bagis
Affiliations:
Dept. of Electronic Eng., Erciyes University, Kayseri, Turkey
Venue:
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Year:
2006

Citing 4
Cited 0

System identification

System identification
Identification of linear dynamic systems using piecewise constant excitations: use, misuse and alternatives

Automatica (Journal of IFAC)
Frequency-domain system identification using non-parametric noise models estimated from a small number of data sets

Automatica (Journal of IFAC)
Intelligent Optimisation Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks

Intelligent Optimisation Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a performance comparison of the genetic and tabu search algorithm for system identification operations of different processes. The identification procedure is based on open-loop step response analysis of the processes. Each of the two algorithms is applied to determine the optimal parameter values of the processes to be modelled. Simulation results demonstrated that the presented algorithms can be efficiently used in the identification problems.