Machine-Learning Approach for Design of Nanomagnetic-Based Antennas
- 219 Downloads
We propose a machine-learning approach for design of planar inverted-F antennas with a magneto-dielectric nanocomposite substrate. It is shown that machine-learning techniques can be efficiently used to characterize nanomagnetic-based antennas by accurately mapping the particle radius and volume fraction of the nanomagnetic material to antenna parameters such as gain, bandwidth, radiation efficiency, and resonant frequency. A modified mixing rule model is also presented. In addition, the inverse problem is addressed through machine learning as well, where given the antenna parameters, the corresponding design space of possible material parameters is identified.
KeywordsAntenna machine learning magneto-dielectric nanomaterial
Unable to display preview. Download preview PDF.
- 1.C. Gianfagna, M. Swaminathan, P.M. Raj, R. Tummala, and G. Antonini, Nanotechnology Materials and Devices Conference (NMDC), 2015 IEEE 10th, pp. 1–5. doi: 10.1109/NMDC.2015.7439256.
- 3.J. Huang, F. Ma, X. Jiang, H. Wang, and J. Li, J. Magn. Magn. Mater. 331, 151 (2013)Google Scholar
- 4.K.N. Rozanov, M.Y. Koledintseva, and J.L. Drewniak, J. Magn. Magn. Mater. 324, 1063 (2012).Google Scholar
- 5.Ph. Toneguzzo, O. Acher, G. Viau, F. Fiévet-Vincent, and F. Fiévet, J. Appl. Phys., 81(8), 5546 (1997).Google Scholar
- 8.G. Pilania, C. Wang, X. Jiang, S. Rajasekaran, and R. Ramprasad, in Sci. Rep. 3, 2810.Google Scholar
- 9.K.N. Rozanov, M.Y. Koledintseva, and J.L. Drewniak, URSI International Symposium on Electromagnetic Theory (EMTS) (2010), pp. 584–587. doi: 10.1109/URSI-EMTS.2010.5637159
- 10.R. Bikky, N. Badi, and A. Bensaoula, COMSOL Conf. 2010.Google Scholar
- 13.CST Microwave Studio, Computer Simulation Technology. http://www.cst.com/Products/CSTMWS. Accessed 10 July 2015
- 14.G. Montavon, M. Rupp, V. Gobre, A. Vazquez-Mayagoitia, K. Hansen, A. Tkatchenko, K.R. Muller, and O. A. von Lilienfeld, New J. Phys. 13 (2013I. doi: 10.1088/1367-2630/15/9/095003
- 15.S. Haykin, Neural Networks and Learning Machines, 3rd ed. (Upper Saddle River, NJ: Pearson - Prentice Hill, 2009), pp. 1–46.Google Scholar
- 17.D. Michie, D.J. Spiegelhalter, and C.C. Taylor, Machine Learning, Neural and Statistical Classification (Upper Saddle River: Ellis Horwood, 1994), pp. 98–99.Google Scholar
- 18.Neural Network Toolbox—Matlab. https://www.mathworks. com/products/neural-network.html. Accessed 10 July 2015