Prediction of giant magneto-impedance effect in amorphous glass-coated micro-wires using artificial neural network
© Kaya; licensee Springer 2013
Received: 31 January 2013
Accepted: 19 April 2013
Published: 30 April 2013
This paper deals with a prediction of a giant magneto-impedance (GMI) effect on amorphous micro-wires using an artificial neural network (ANN). The prediction model has three hidden layers with fifteen neurons and full connectivity between them. The ANN model is used to predict the GMI effect for Co70.3Fe3.7B10Si13Cr3 glass-coated micro-wire. The results show that the ANN model has a 98.99% correlation with experimental data.
where , and are the GMI ratio, magneto impedance at magnetic field H, and magneto impedance at maximum magnetic field, respectively. The discovery of the giant magneto-impedance (GMI) effect in soft ferromagnetic amorphous micro-wires makes them very attractive candidate material for making high-performance magnetic sensors [1–6]. The use of such micro-wires could also have implications for the next generation of electronic devices, which will involve increasingly smaller components [7–9]. The GMI effect is termed the giant variation of impedance of a ferromagnetic conductor with an ac current in an applied dc magnetic field [10, 11].
Artificial neural networks are increasingly becoming useful in prediction of magnetic performance in electromagnetic devices. The ANN operates like a human brain. ANNs have been applied in many areas (such as electronics, forecasting, banking and aerospace) because of these features. The available ANN software today provides many neural network architectures and learning algorithms, and it also helps users to apply ANN to their specific problems easily [12, 13].
In this investigation, the GMI effects are modeled using self-organizing feature map (SOFM) and previous experimental data  of Co70.3Fe3.7B10Si13Cr3 amorphous glass-coated micro-wire.
Kohonen neural network
The ideas of self-organizing feature map (SOFM) are rooted in competitive learning networks. The SOFM transforms the input of an arbitrary dimension into a one- or two-dimensional discrete map subject to a topological (neighborhood-preserving) constraint. The feature maps are computed using Kohonen unsupervised learning. The output of the SOFM can be used as the input to a supervised classification neural network .
ANN model for glass-coated amorphous micro-wire
The input parameters were magnetizing field , wire length and frequency . A total of 1,200 input vectors obtained from varied samples  were available in the training data. The number of hidden layers and neurons in each layer were determined through trial and error to be optimal including different transfer functions such as hyperbolic tangent, sigmoid and hybrid. After the network was trained, a better result was obtained from the network formed by the hyperbolic tangent transfer function in the hidden and output layers. The number of epochs was 106 for training.
Results and discussion
Comparison of predicted and measured GMI% values
Max GMI% (measured)
Max GMI% (predicted)
10 mm-1 MHz
10 mm-2 MHz
10 mm-4 MHz
10 mm-10 MHz
All statistical values prove that the proposed ANN model is suitable to predict the GMI values very close to the experimental results.
The proposed model developed from experimental data obtained previously can be used to predict more easily the GMI curves for Co70.3Fe3.7B10Si13Cr3 amorphous glass-coated micro-wires (5, 10 and 15 mm length) at 1, 2, 4 and 10 MHz. According to the results obtained from modeling a neural network, the neural network with the architecture of the ANN method is suitable for predicting the GMI values of Co-based amorphous glass-covered micro-wire. The overall prediction error is about 1% compared with the measured GMI values.
Dedicated to Professor Hari M Srivastava.
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