Abstract
Over the past decade, artificial neural networks have emerged as fast computational medium for predicting different performance parameters of microstrip antennas due to their learning and generalization features. This paper illustrates a neural network model for instantly predicting the resonance frequencies, gains, directivities, antenna efficiencies, and radiation efficiencies for dual-frequency operation of slotted microstrip antennas with air-gap. The proposed neural model is valid for any arbitrary slot-dimensions and inserted air-gap within their specified ranges. A prototype is fabricated using Roger’s substrate and its performance is measured for validation. A very good agreement is achieved in simulated, predicted, and measured results.
Highlights
There are many situations of wireless communication where dual-frequency operation is required such as satellite communication, radar systems, and global positioning system (GPS)
Different researchers have proposed different techniques for obtaining dual resonance such as multilayered stacked patch [2, 3], slotted rectangular patch [4], square patch with notches [5], patch loaded with shorting posts [6] or varactor diodes [7], and rectangular patch fed by an inclined slot [8]
The requirement for having a new solution for every small alteration in the geometry as well as the problems associated with the thickness of the substrates in analytical methods leads to complexities and processing cost [9]
Summary
There are many situations of wireless communication where dual-frequency operation is required such as satellite communication, radar systems, and global positioning system (GPS). The choice of test functions and path integrations appear to be more critical without initial assumptions in the final stage of the numerical results These approaches require a new solution even for an infinitesimal alteration in the geometry. Simultaneous computations of different performance parameters (i.e., resonance frequency, gain, directivity, antenna efficiency, and radiation efficiency) using neural networks model have been rarely attempted in the available literature [9,10,11,12,13,14,15,16,17,18,19,20,21,22,23] as these parameters are essentially required for antenna designers for synthesizing the MSAs. In the proposed work, the authors have extended their earlier works of generalized neural networks modeling [24,25,26,27,28,29,30] for predicting different performance parameters (i.e., resonance frequencies, gains, directivities, antenna efficiencies, and radiation efficiencies) of slotted microstrip antennas for dualfrequency operation.
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