Published February 1, 2006 | Version v1
Journal article

A neural network approach for determination of Preisach model parameters under a sinusoidal induction at various frequencies

  • 1. Laboratory of the Electromagnetic Systems-UER Electrotechnic EMP (ex- ENITA), BP 17 Bordj El Bahri (Algeria)
  • 2. School of Engineering Electronic, Electrical and Computer Engineering University of Birmingham, Edgbaston B15 2TT (United Kingdom)

Description

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

Additional details

Identifiers

DOI
10.1016/j.physb.2005.10.027;
PII
S0921-4526(05)01056-2;

Publishing Information

Journal Title
Physica. B, Condensed Matter
Journal Volume
372
Journal Issue
1-2
Journal Page Range
p. 106-110
ISSN
0921-4526
CODEN
PHYBE3

Conference

Title
5. international symposium on hysteresis and micromagnetic modeling
Dates
30 May - 1 Jun 2005
Place
Budapest (Hungary)

INIS

Country of Publication
Netherlands
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
37069952
Subject category
S75: CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY;
Resource subtype / Literary indicator
Conference
Descriptors DEI
DIAGRAMS; FLUX DENSITY; HYSTERESIS; INDUCTION; LOSSES; MAGNETIZATION; MATHEMATICAL MODELS; NEURAL NETWORKS
Descriptors DEC
INFORMATION

Optional Information

Copyright
Copyright (c) 2005 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.