An explainable neural network integrating Jiles-Atherton and nonlinear auto-regressive exogenous models for modeling universal hysteresis

Ni, LeiChen, JieChen, GuoqiangZhao, DongmeiWang, GengAphale, Sumeet S.2024-07-022024-07-022024-10-01Ni, L, Chen, J, Chen, G, Zhao, D, Wang, G & Aphale, S S 2024, 'An explainable neural network integrating Jiles-Atherton and nonlinear auto-regressive exogenous models for modeling universal hysteresis', Engineering Applications of Artificial Intelligence, vol. 136, no. Part A, 108904. https://doi.org/10.1016/j.engappai.2024.1089040952-1976ORCID: /0000-0002-1691-1648/work/163196839https://hdl.handle.net/2164/23745Acknowledgements This work is supported by the Key Technologies R&D Program of Sichuan Province, China under Grant No. 23ZDYF0471, the Doctoral Research Fund of Southwest University of Science and Technology under Grant No. 22zx7140. The authors also thank the anonymous reviewers for their insightful and constructive comments172719896engPiezoelectric actuatorRate-dependent hysteresisNeural networkJiles-atherton modelAsymmetricExplainabilityTA Engineering (General). Civil engineering (General)TAAn explainable neural network integrating Jiles-Atherton and nonlinear auto-regressive exogenous models for modeling universal hysteresisJournal article10.1016/j.engappai.2024.108904136