A Neural Approach for Determination of Preisach Model Parameters Under a Sinusoidal Induction at Various Frequencies

D Moussaoui; A Bendjerad; Mourad Oussalah; H Houassine

doi:10.1016/j.physb.2005.10.027

A Neural Approach for Determination of Preisach Model Parameters Under a Sinusoidal Induction at Various Frequencies

D Moussaoui
, A Bendjerad
, Mourad Oussalah
, H Houassine

Electronic, Electrical and Systems Engineering

Research output: Contribution to journal › Article

8 Citations (Scopus)

Abstract

In this paper, we present a neural network-based approach, which allows us to predict the hysteretic loop, whatever the value of the frequency and flux density. The approach makes use of the Preisach-hysteretic model (PM) which provides a mathematical model to the B(H) curve, while the neural enables us to identify and predict the behaviour of parameters that the PM needs. (c) 2005 Elsevier B.V. All rights reserved.

Original language	English
Pages (from-to)	106-110
Number of pages	5
Journal	Physica B - Physics of Condensed Matter
Volume	372
Issue number	1-2
DOIs	https://doi.org/10.1016/j.physb.2005.10.027
Publication status	Published - 1 Feb 2006

Keywords

magnetic losses
hysteresis
neural network
Preisach model

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10.1016/j.physb.2005.10.027

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abstract = "In this paper, we present a neural network-based approach, which allows us to predict the hysteretic loop, whatever the value of the frequency and flux density. The approach makes use of the Preisach-hysteretic model (PM) which provides a mathematical model to the B(H) curve, while the neural enables us to identify and predict the behaviour of parameters that the PM needs. (c) 2005 Elsevier B.V. All rights reserved.",

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AU - Houassine, H

PY - 2006/2/1

Y1 - 2006/2/1

N2 - In this paper, we present a neural network-based approach, which allows us to predict the hysteretic loop, whatever the value of the frequency and flux density. The approach makes use of the Preisach-hysteretic model (PM) which provides a mathematical model to the B(H) curve, while the neural enables us to identify and predict the behaviour of parameters that the PM needs. (c) 2005 Elsevier B.V. All rights reserved.

AB - In this paper, we present a neural network-based approach, which allows us to predict the hysteretic loop, whatever the value of the frequency and flux density. The approach makes use of the Preisach-hysteretic model (PM) which provides a mathematical model to the B(H) curve, while the neural enables us to identify and predict the behaviour of parameters that the PM needs. (c) 2005 Elsevier B.V. All rights reserved.

KW - magnetic losses

KW - hysteresis

KW - neural network

KW - Preisach model

UR - https://www.scopus.com/pages/publications/30844443248

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DO - 10.1016/j.physb.2005.10.027

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JO - Physica B - Physics of Condensed Matter

JF - Physica B - Physics of Condensed Matter

IS - 1-2

ER -

A Neural Approach for Determination of Preisach Model Parameters Under a Sinusoidal Induction at Various Frequencies

Abstract

Keywords

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