De Alcantara, N. P. [UNESP]2014-05-272014-05-272008-12-01Proceedings of the IASTED International Conference on Intelligent Systems and Control, p. 99-104.1025-8973http://hdl.handle.net/11449/70785This paper uses artificial neural networks (ANN) to compute the resonance frequencies of rectangular microstrip antennas (MSA), used in mobile communications. Perceptron Multi-layers (PML) networks were used, with the Quasi-Newton method proposed by Broyden, Fletcher, Goldfarb and Shanno (BFGS). Due to the nature of the problem, two hundred and fifty networks were trained, and the resonance frequency for each test antenna was calculated by statistical methods. The estimate resonance frequencies for six test antennas were compared with others results obtained by deterministic and ANN based empirical models from the literature, and presented a better agreement with the experimental values.99-104engMicrostrips antennasMobile communicationsNeural networksResonance frequencyArtificial Neural NetworkBroydenEmpirical modelExperimental valuesMicro-stripsPerceptronQuasi-Newton methodsRectangular-microstrip antennasResonance frequenciesTest antennaIntelligent systemsMicrostrip antennasMicrowave antennasMobile telecommunication systemsNatural frequenciesNewton-Raphson methodNumerical methodsWireless networksMobile antennasTrabalho apresentado em eventoAcesso aberto2-s2.0-74549155770