The 2003 Congress on Evolutionary Computation, 2003. CEC '03.
DOI: 10.1109/cec.2003.1299748
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Parameter identification of induction motors using differential evolution

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Cited by 94 publications
(58 citation statements)
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“…Perry et al (2006) presented a modified GA to identify structural systems. DE has been successfully applied in induction motor identification problems (Ursem and Vadstrup (2003)) and structural system identification ).…”
Section: Introductionmentioning
confidence: 99%
“…Perry et al (2006) presented a modified GA to identify structural systems. DE has been successfully applied in induction motor identification problems (Ursem and Vadstrup (2003)) and structural system identification ).…”
Section: Introductionmentioning
confidence: 99%
“…These include: non-linear least squares, Kalman Filters [5], genetic algorithms (GA) [6]- [8], local search algorithms (LSA), simulated annealing (SA), differential evolution and various forms of particle swarm optimization (PSO) [9]- [12]. Most of these techniques are applied to data gathered during the startup of the machine [7], [13], [10], [11]. Some approaches compare the results of the estimated model to data gathered by applying mechanical tests to the machine [12], [14].…”
Section: Introductionmentioning
confidence: 99%
“…Then, they explored previously conducted eight benchmark-function-based comparative studies (namely, unconstraint optimization [Storn and Price 1997b;Paterlini and Krink 2004;Vesterstroem and Thomsen 2004;Ali and Törn 2004], multi-constraints nonlinear optimization [Lampinen 2004], mixed-variable optimization [Lampinen and Zelinka 1999b], multi-objective optimization [Kukkonen and Lampinen 2004], noisy-function optimization ) and also eleven application-oriented performance comparison studies (namely, multi-sensor fusion [Joshi and Sanderson 1999], earthquake relocation [Bohuslav and Michal 2001], DC operating point analysis for nonlinear circuits [Crutchley and Zwolinski 2004], estimation of heat transfer parameters in a trickle-bed reactor [Babu and Sastry 1999], aerodynamic optimization [Rogalsky et al 1999], image registration [Salomon 2001], identifying induction motor parameters [Ursem and Vadstrup 2004], optimization of neural networks [Fischer et al 1999;Plagianakos et al 2001], and optimization of carbon and silicon cluster geometry [Ali and Törn 2000;Chakraborti et al 2002]). Finally, they concluded as follows:…”
Section: Why Differential Evolution?mentioning
confidence: 99%