A machine learning approach to design with magneto dielectric nano-composite (MDNC) substrate for planar inverted-F antenna (PIFA) is presented. A new mixing rule model has been developed. A database of material properties has been created using several particle radius and volume fraction. A second database built with antenna simulations has been developed to complete the machine learning dataset. It is shown that, starting from particle radius and volume fraction of the nano-magnetic material, it is possible to calculate the antenna parameters like gain, bandwidth, radiation efficiency, resonant frequency, and viceversa with good precision by using machine learning techniques.

Enabling antenna design with nano-magnetic materials using machine learning

Gianfagna C.;Antonini G.
2016

Abstract

A machine learning approach to design with magneto dielectric nano-composite (MDNC) substrate for planar inverted-F antenna (PIFA) is presented. A new mixing rule model has been developed. A database of material properties has been created using several particle radius and volume fraction. A second database built with antenna simulations has been developed to complete the machine learning dataset. It is shown that, starting from particle radius and volume fraction of the nano-magnetic material, it is possible to calculate the antenna parameters like gain, bandwidth, radiation efficiency, resonant frequency, and viceversa with good precision by using machine learning techniques.
978-1-4673-9362-1
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/175678
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