Yayın:
Prediction of giant magneto impedance on As-cast and post production treated Fe4.3Co68.2Si12.5B15 amorphous wires using neural network

Placeholder

Tarih

Akademik Birimler

Kurum Yazarları

Çaylak, Osman
Derebaşı, Naim

Yazarlar

Danışman

Dil

Yayıncı:

Natl Inst Optoelectronics

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Özet

A giant magneto impedance effect was experimentally measured on as-cast and post production treated amorphous wires although it takes some time due to varying measuring condition such as sample, static magnetic field and frequency. Measured data from different as-cast and post production treated samples was used for training of the network. A 3-node input layer, 1-node output layer neural network model with 3 hidden layers and full connectivity between nodes were developed. A total of 1600 input vectors obtained from varied samples were available in the training set. The network was formed by hybrid transfer functions and 21 numbers of nodes in the hidden layers, after the performance of many models were tried. A set of test data, different from the training data set was used to investigate the network performance. The average correlation and prediction error of giant magneto impedance effect were found to be 99% and 1% for tested Fe4.3Co68.2 Si12.5B15 amorphous wires.

Açıklama

Bu çalışma, 07-09 Haziran 2008 tarihleri arasında Constanta[Romanya]’da düzenlenen 9. International Balkan Workshop on Applied Physics bildiri olarak sunulmuştur.

Kaynak:

Anahtar Kelimeler:

Konusu

Materials science, Optics, Physics, Amorphous wire, Artificial neural network, Giant magneto impedance, Cobalt compounds, Iron compounds, Magnetic anisotropy, Multilayer neural networks, Network layers, Neural networks, Silicon compounds, Statistical tests, Wire, Amorphous wire, Correlation and prediction, Full connectivities, Giant magneto impedance, Giant magneto impedance effect, Measuring conditions, Neural network model, Static magnetic fields, Boron compounds, Dual-phase steels, Martensite, Tensile, Microstructure, Morphology

Alıntı

Çaylak, O. ve Derebaşı, N. (2008). "Prediction of giant magneto impedance on As-cast and post production treated Fe4.3Co68.2Si12.5B15 amorphous wires using neural network". Journal of Optoelectronics and Advanced Materials, 10(11), 2916-2918.

Endorsement

Review

Supplemented By

Referenced By

3

Views

0

Downloads