Artificial Neural Network (ANN) have been recently proposed as a mean to speed up the optimized design procedure of printed Reflectarrays, creating a surrogate model of a patch radiator as a function of its geometric parameters, the angle of incidence and frequency. This paper presents an improvement of ANN learning procedure by hybridising classical Error Back-Propagation with Meta Particle Swarm Optimization algorithm. In this way the ANN learning procedure proved to converge in a much more effective way, i.e. with the necessity of the introduction of a smaller size set of training samples and with a significant reduction of the computational effort and of the data memory storage.

Modeling of reflectarray elements by means of MetaPSO-based Artificial Neural Network

MUSSETTA, MARCO;ZICH, RICCARDO
2013-01-01

Abstract

Artificial Neural Network (ANN) have been recently proposed as a mean to speed up the optimized design procedure of printed Reflectarrays, creating a surrogate model of a patch radiator as a function of its geometric parameters, the angle of incidence and frequency. This paper presents an improvement of ANN learning procedure by hybridising classical Error Back-Propagation with Meta Particle Swarm Optimization algorithm. In this way the ANN learning procedure proved to converge in a much more effective way, i.e. with the necessity of the introduction of a smaller size set of training samples and with a significant reduction of the computational effort and of the data memory storage.
2013
7th European Conference on Antennas and Propagation (EuCAP)
9788890701832
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/759465
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 2
social impact