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Using neural networks in the identification of Preisach-type hysteresis models
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inferred← citeshttps://doi.org/10.1016/j.engappai.2023.105940openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/icca.2005.1528105openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/icca.2010.5524289openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/icma.2007.4303962openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/icma.2008.4798824openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/intmag.2002.1001348openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/intmag.2002.1001349openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/isda.2006.253804openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/isda.2006.72openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/isic.2003.1254704openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/isic.2004.1387708openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/melecon48756.2020.9140585openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/melecon48756.2020.9140623openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/robio.2009.4913134openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/robot.2005.1570674openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/spawda48812.2019.9019229openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tcst.2007.906274openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tcst.2008.924580openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tcst.2010.2049999openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tcst.2012.2206029openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tmag.2004.829250openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tmag.2004.833427openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tmag.2008.2001657openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tmag.2011.2176106openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tmag.2017.2702168openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tmag.2019.2898888openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tmag.2019.2942804openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/tpel.2026.3659820openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/wcica.2006.1712624openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/wcica.2006.1712647openalex-snapshot
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inferred← citeshttps://doi.org/10.1140/epjp/i2019-12883-7openalex-snapshot
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inferred← citeshttps://doi.org/10.1143/jjap.51.09md01openalex-snapshot
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inferred← citeshttps://doi.org/10.1155/2011/208517openalex-snapshot
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inferred← citeshttps://doi.org/10.1155/2011/458768openalex-snapshot
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inferred← citeshttps://doi.org/10.1155/2014/150345openalex-snapshot
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inferred← citeshttps://doi.org/10.1177/0954406215627828openalex-snapshot
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inferred← citeshttps://doi.org/10.1177/1045389x08093547openalex-snapshot
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inferred← citeshttps://doi.org/10.18090/samriddhi.v13i01.11openalex-snapshot
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inferred← citeshttps://doi.org/10.2316/journal.205.2007.3.205-4375openalex-snapshot
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inferred← citeshttps://doi.org/10.23919/cje.2021.00.273openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/app14010321openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/s23167293openalex-snapshot
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inferred← citeshttps://doi.org/10.3844/ajnsp.2010.1.6openalex-snapshot
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