A neural network inverse procedure has been proposed to solve the Moving Vector Hysteron Model identification. Using the analytic modified Preisach model, a multilayer perceptron neural network is firstly trained to associate the direct relationship among the model parameters and the hysteretic behavior of the material to be modeled both in case of scalar and rotational magnetization. The neural model is then inverted using as input the magnetization identifying the corresponding model parameters values. Model validations with experimental tests and simulations will be performed.

Moving vector hysteron model identification based on neural network inversion

CARCANGIU, SARA;FANNI, ALESSANDRA;MONTISCI, AUGUSTO;
2016

Abstract

A neural network inverse procedure has been proposed to solve the Moving Vector Hysteron Model identification. Using the analytic modified Preisach model, a multilayer perceptron neural network is firstly trained to associate the direct relationship among the model parameters and the hysteretic behavior of the material to be modeled both in case of scalar and rotational magnetization. The neural model is then inverted using as input the magnetization identifying the corresponding model parameters values. Model validations with experimental tests and simulations will be performed.
9781509011315
Hysteresis models; Inverse Models; Material Magnetization; Model identification; Neural networks; Energy Engineering and Power Technology; Biomedical Engineering; Instrumentation; Computer Networks and Communications; Computer Science Applications; Computer Vision and Pattern Recognition; Human Factors and Ergonomics
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/212927
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