Title :
Neural network training schemes for antenna optimization
Author :
Linh Ho Manh ; Grimaccia, F. ; Mussetta, M. ; Zich, Riccardo E.
Author_Institution :
Dipt. di Energia, Politec. di Milano, Milan, Italy
Abstract :
Thanks to the advantage of low profile and low cost, microstrip ring antenna design has been an interesting and challenging issue in modern engineering society. The trade-off among all the degrees of freedom becomes quite complex and direct antenna synthesis by full-wave analysis are often not applicable. In optimization scheme, the associated cost function by computational approach is always expensive and time-consuming. Artificial Neural Network (ANN) has been exploit as a modeling methodology in Electromagnetic field in recent years. In this article, a new approach with the aim of boosting “online-trading information” between the global optimizer and ANN surrogate model will be discussed.
Keywords :
electrical engineering computing; microstrip antennas; neural nets; ANN surrogate model; antenna optimization scheme; artificial neural network; degrees of freedom; direct antenna synthesis; electromagnetic field modeling methodology; full-wave analysis; global optimizer; low cost microstrip ring antenna design; low profile microstrip ring antenna design; neural network training schemes; online-trading information; Artificial neural networks; Biological neural networks; Computational modeling; Microstrip antennas; Optimization; Training;
Conference_Titel :
Antennas and Propagation Society International Symposium (APSURSI), 2014 IEEE
Conference_Location :
Memphis, TN
Print_ISBN :
978-1-4799-3538-3
DOI :
10.1109/APS.2014.6905301