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Description
  • Many systems with hysteresis are adequately represented by the Preisach model. Our target is to find a probability density function of this model just from the knowledge of hysteresis curve, it means only from the dependence of the output signal on the input signal. In this paper we introduce few numerical iterative methods to find this PM space density function.
  • Many systems with hysteresis are adequately represented by the Preisach model. Our target is to find a probability density function of this model just from the knowledge of hysteresis curve, it means only from the dependence of the output signal on the input signal. In this paper we introduce few numerical iterative methods to find this PM space density function. (en)
Title
  • Iterative algorithms for divergence based density identification in Preisach model
  • Iterative algorithms for divergence based density identification in Preisach model (en)
skos:prefLabel
  • Iterative algorithms for divergence based density identification in Preisach model
  • Iterative algorithms for divergence based density identification in Preisach model (en)
skos:notation
  • RIV/68407700:21340/13:00210458!RIV14-MSM-21340___
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  • S, Z(MSM6840770039)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
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  • 81376
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  • RIV/68407700:21340/13:00210458
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  • Nonlinear nonclassical materials; PM space; density identification; iterative algorithms (en)
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  • [CD8C964E0241]
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  • Nebřich
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  • Praha
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  • SPMS 2013 Stochastic and Physical Monitoring Systems Proceedings
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http://linked.open...ichTvurcuVysledku
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  • Kůs, Václav
  • Dos Santos, S.
  • Papoušková, Jana
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number of pages
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  • České vysoké učení technické v Praze
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  • 978-80-01-05383-6
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  • 21340
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