2001 · 11 citations · 0 references
EngineeringElectrical EngineeringMathematicsComputational ElectromagneticsMachine LearningOptimizationError EstimationOptimization ModelingSmall-signal AnalysisSystem IdentificationHysteresisHysteresis CompensationHysteresis EffectsHysteresis ModelingNonlinear ModelingParameter IdentificationSensor HysteresisSmall-signal Parameter Extraction
Abstract In this paper we consider the abstract mathematical problem of identifying a Preisach operator from idealized training data. We give sufficient conditions on the training data to identify an optimal approximation to the operator. We also show that it is possible to introduce errors if one tries to approximate the operator in the “wrong” function space. We demonstrate the utility of these results using experimental data from SMA wires. We identify the optimal operator using this data and evaluate the performance of the operator in temperature controlled experiments.