Bouc–Wen model parameter identification for a MR fluid damper using computationally efficient GA

Dyke, 1996, Modeling and control of magnetorheological dampers for seismic response reduction, Smart Materials and Structures, 565, 10.1088/0964-1726/5/5/006

Spencer, 1997, Controlling buildings: A new frontier in feedback, IEEE Control Systems Magazine, 17, 19, 10.1109/37.642972

Jansen, 2000, Semiactive control strategies for MR dampers: Comparative study, Journal of Engineering Mechanics, 126, 795, 10.1061/(ASCE)0733-9399(2000)126:8(795)

Spencer, 1997, Phenomenological model for magnetorheological dampers, Journal of Engineering Mechanics, 123, 230, 10.1061/(ASCE)0733-9399(1997)123:3(230)

Butz, 2002, Modelling and simulation of electro- and magnetorheological fluid dampers, Journal of Applied Mathematics and Mechanics, 82, 3

Yang, 2004, Dynamic modeling of large-scale magnetorheological damper systems for civil engineering applications, Journal of Engineering Mechanics, 130, 1107, 10.1061/(ASCE)0733-9399(2004)130:9(1107)

Schurter KC, Roschke PN. Fuzzy modelling of a magnetorheological damper using ANFIS. In: Proc. 9th IEEE intl conf on fuzzy systems. 2000. p. 122–7

Wang, 2005, Modeling and control of magnetorheological fluid dampers using neural networks, Smart Materials and Structures, 14, 111, 10.1088/0964-1726/14/1/011

Choi, 2001, A hysteresis model for the field-dependent damping force of a magnetorheological damper, Journal of Sound and Vibration, 245, 375, 10.1006/jsvi.2000.3539

Ma XQ, Wang ER, Rakheja S, Su CY. Modeling hysteretic characteristics of MR-fluid damper and model validation. In: Proc. 41st IEEE conf. on decision and control. 2002. p. 1675–80

Jin, 2005, Nonlinear blackbox modeling of MR-dampers for civil structural control, IEEE Transactions on Control Systems Technology, 13, 345, 10.1109/TCST.2004.841645

Song, 2005, Modeling magnetorheological dampers with application of nonparametric approach, Journal of Intelligent Material Systems and Structures, 421, 10.1177/1045389X05051071

Fulginei, 2005, Softcomputing for the identification of the Jiles-Atherton model parameters, IEEE Transactions on Magnetics, 41, 1100, 10.1109/TMAG.2004.843345

Giuclea, 2004, Model parameter identification of vehicle vibration control with magnetorheological dampers using computational intelligent methods, Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering, 218, 569, 10.1243/0959651042715240

Goldberg, 1989

Herrera, 1998, Tackling real-coded genetic algorithms: Operators and tools for behavioural analysis, Artificial Intelligence Review, 12, 265, 10.1023/A:1006504901164

Goldberg, 1991, A comparative analysis of selection schemes used in genetic algorithms, 69, 10.1016/B978-0-08-050684-5.50008-2

Baker JE. Reducing bias and inefficiency in the selection algorithm. In: Proc. 2nd intl. conf. on genetic algorithms. Cambridge (MA); 1987. p. 14–21

Eiben, 1999, Parameter control in evolutionary algorithms, IEEE Transactions on Evolutionary Computation, 3, 124, 10.1109/4235.771166

Kwok NM, Ha QP, Li J, Samali B, Hong SM. Parameter identification for a magnetorheological fluid damper: An evolutionary computation approach. In: Proc. 6th intl. conf. on intelligent technologies. 2005. p. 115–22

Bossis, 2002, Magnetorheological fluids, Journal of Magnetism and Magnetic Materials, 224, 10.1016/S0304-8853(02)00680-7

Dominguez, 2004, Modelling the hysteresis phenomenon of magnetorheological dampers, Smart Materials and Structures, 1351, 10.1088/0964-1726/13/6/008

Rogers, 1999, Genetic drifts in genetic algorithm selection schemes, IEEE Transactions on Evolutionary Computation, 3, 298, 10.1109/4235.797972

Zhang Q, Li X, Tran QA. Breeder genetic algorithm based fuzzy simulation. In: Proc. 5th world congress on intelligent control and automation. 2004. p. 2109–11

Li J, Kang L, Wu Z. An adaptive neighbourhood-based multi-parent crossover operator for real-coded genetic algorithms. In: Proc. 2003 congress on evolutionary computation. 2003. p. 14–21

Rudolph, 1994, Convergence analysis of canonical genetic algorithms, IEEE Transactions on Neural Networks, 5, 96, 10.1109/72.265964