Dynamic hysteresis modelling for nano-crystalline cores

No Thumbnail Available

Date

2009-03

Authors

Journal Title

Journal ISSN

Volume Title

Publisher

Pergamon-Elsevier Science

Abstract

This paper presents all artificial neural network approach based oil dynamic Preisach model to compute hysteresis loops of nano-crystalline cores. The network has been trained by a Levenberg-Marquardt learning algorithm. The model is fast and does not require tremendous computational efforts. The results obtained by using the proposed model are in good agreement with experimental results.

Description

Keywords

Dynamic hysteresis model, Nano-crystal, Neural network, Neural-network, Genetic algorithm, Toroidal cores, Power losses, Prediction, Computer science, Engineering, Operations research & management science, Crystalline materials, Hysteresis loops, Neural networks, Artificial neural network approach, Computational effort, Dynamic hysteresis modeling, Dynamic hysteresis modelling, Levenberg-Marquardt learning algorithms, Nanocrystalline cores, ON dynamics, Hysteresis

Citation

Küçük, İ. S. vd. (2009). "Dynamic hysteresis modelling for nano-crystalline cores". Expert Systems with Applications, 36(2), 3188-3190.