@article {4011,
	title = {Neurospectral computation for the resonant characteristics of microstrip patch antenna printed on uniaxially anisotropic substrates},
	journal = {International Journal of Microwave and Wireless Technologies},
	volume = {9},
	number = {3},
	year = {2017},
	pages = {613-620},
	abstract = {
	
		
			
				
					Modeling and design of rectangular microstrip patch printed on isotropic or anisotropic substrate are accomplished in this paper. The use of spectral domain method in conjunction with artificial neural networks (ANNs) to compute the resonant characteristics of rectangular microstrip patch printed on isotropic or anisotropic substrates. The moment method implemented in the spectral domain offers good accurateness, but its computational cost is high owing to the evaluation of the slowly decaying integrals and the iterative nature of the solution process. The paper introduces the electromagnetic knowledge combined with ANN in the analysis of rectangular microstrip antenna on uniaxially anisotropic substrate to reduce the complexity of the spectral domain method and to minimize the CPU time necessary to obtain the numerical results. The numerical comparison between neurospectral and conventional moment methods shows significant improvements in time convergence and computational cost. Hence, the use of neurospectral approach presented here as a promising fast technique in the design of microstrip antennas.
				
			
		
	



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},
	url = {https://www.cambridge.org/core/journals/international-journal-of-microwave-and-wireless-technologies/article/neurospectral-computation-for-the-resonant-characteristics-of-microstrip-patch-antenna-printed-on-uniaxially-anisotropic-substrates/859FA2B6042DA2},
	author = {Barkat, Lamia and Bedra, Sami and Fortaki , Tarek and Bedra , Randa}
}
