Novel Neural Network Models of Q-Type Integrals and Their Use for Circular-loop Antenna Analysis
In this paper, the neural network methodology was applied to develop analytical models, defined by means of weighted sums of basis functions, to approximate/interpolate the class of Q-type power-radiation integrals arising in antenna theory. The associated problems in this proposed approach, I.e., the choice of the type and number of basis functions, and the models parameters optimization, were replaced by the problem of training weights in neural networks. The better neural model resultant of this research (obtained through of the damped-sinusoid basis functions set) was used for efficient evaluations of the circular-loop antenna radiation resistance and directivity. The neural model accuracy and computational efficiency were compared with recent approaches published in literature.
Humberto César Chaves Fernandes M. G. Passos P. H. da F. Silva
UFRN/TECFOTON-Campus Universitário, CEP: 59072-970- Natal, RN, Brazil CEFET-PB/GTEMA-Av. 1°de Maio, 720 Jaguaribe, CEP: 58015-430-Jo(a)o Pessoa, PB, Brazil
国际会议
Progress in Electromagnetics Research Symposium 2007(2007年电磁学研究新进展学术研讨会)(PIERS 2007)
北京
英文
2137-2141
2007-03-26（万方平台首次上网日期，不代表论文的发表时间）