References
- S.-L. Xie et al., Modeling and compensation of asymmetric hysteresis for pneumatic artificial muscles with a modified generalized Prandtl–Ishlinskii model, Mechatronics 52, 49 (2018). DOI: https://doi.org/10.1016/j.mechatronics.2018.04.001.
- D. C. Jiles, and D. L. Atherton, Theory of ferromagnetic hysteresis, J. Appl. Phys. 55 (6), 2115 (1984). DOI: https://doi.org/10.1063/1.333582.
- I. D. Mayergoyz, Generalized Scalar Preisach Models of Hysteresis, in Mathematical Models of Hysteresis and Their Applications (A Volume in Electromagnetism) (Academic Press: San Francisco, CA, 2003), pp. 65–147. DOI:https://doi.org/10.1016/B978-012480873-7/50003-7.
- M. Farrokh, and M. Shafiei Dizaji, Adaptive simulation of hysteresis using neuro-Madelung model, J. Intell. Mater. Syst. Struct. 27 (13), 1713 (2016). DOI: https://doi.org/10.1177/1045389X15604283.
- X. Tan, and J. S. Baras, Modeling and control of hysteresis in magnetostrictive actuators, in Proceedings of the 41st IEEE Conference on Decision and Control (2002). DOI: https://doi.org/10.1109/CDC.2002.1184616.
- S. Xiao, and Y. Li, Modeling and high dynamic compensating the rate-dependent hysteresis of piezoelectric actuators via a novel modified inverse Preisach model, IEEE Trans. Control Syst. Technol. 21 (5), 1549 (2013). DOI: https://doi.org/10.1109/TCST.2012.2206029.
- Y. Wang, and H. Guo, A BP neural network modeling method based on global error for the hysteresis of piezoelectric actuator, IOP Conf. Ser: Mater. Sci. Eng. 585, 012070 (2019). 585/1/012070 DOI: https://doi.org/10.1088/1757-899X/.
- A. Srivastava, C. Ward, and R. V. Patel, Adaptive neural Preisach model and model predictive control of shape memory alloy actuators, in 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM) (2017), pp. 1179–1184. DOI: https://doi.org/10.1109/AIM.2017.8014178.
- K. An et al., Frequency invariability of (Pb,La)(Zr,Ti)O3 antiferroelectric thick-film micro-cantilevers, Sensors 18 (5), 1542 (2018). DOI: https://doi.org/10.3390/s18051542.
- J. Liu et al., Out-of-plane actuation of silicon micro-cantilever based on (Pb, La) (Zr, Ti)O3 antiferroelectric thick films, J. Mater. Sci.: Mater. Electron. 27 (2), 1758 (2016). DOI: https://doi.org/10.1007/s10854-015-3950-y.
- K. Hornic, Multilayer feedforward networks are universal approximators, Neural Netw. 2 (5), 359 (1989). DOI: https://doi.org/10.1016/0893-6080(89)90020-8.
- G. Cybenko, Approximation by superpositions of a sigmoidal function, Math. Control Signal Syst. 2 (4), 303 (1989). DOI: https://doi.org/10.1007/BF02551274.
- R. P. Singh, M. Dixit, and S. Silakari, Image contrast enhancement using GA and PSO: a survey, in International Conference on Computational Intelligence and Communication Networks, Bhopal (2014), pp. 186–189. DOI: https://doi.org/10.1109/CICN.2014.51.
- C. A. S. Lima et al., Comparison of computational performance of GA and PSO optimization techniques when designing similar systems-Typical PWR core case, Ann. Nucl. Energy 38 (6), 1339 (2011). DOI: https://doi.org/10.1016/j.anucene.2011.02.002.