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. 1. Introduction There are many situations of wireless communication where dual-frequency operation is required such as satellite communication, radar systems, and global positioning system (GPS). Microstrip antennas (MSAs), because of operating in dual-frequency mode, have eliminated two single-frequency operated antennas in these applications [1]. 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]. These methods [2–8] can roughly be categorized as analytical methods and numerical methods. The analytical methods provide a good spontaneous explanation for the operation of MSAs. These techniques are based on the physical assumptions for simplifying the radiation mechanism of the MSAs but are not suitable for many microstrip structures where the thickness of the substrate is not very thin. On the other hand, the numerical methods provide accurate results but only at the cost of using complex mathematical expressions in the form of integral equations. The choice of test functions and path integrations appear to be more critical without initial assumptions in the final stage of the numerical results. Also, these approaches require a new solution even for an infinitesimal alteration in the geometry. Thus, 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]. Recently, artificial neural networks (ANNs) models have acquired tremendous applications in the wireless communication due to their ability and
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