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.