Prediction of Slot-Size and Inserted Air-Gap for Improving the Performance of Rectangular Microstrip Antennas Using Artificial Neural Networks

Artificial neural networks have been getting popularity for predicting various performance parameters of microstrip antennas due to their learning and generalization features. In this letter, a neural-networks-based synthesis model is presented for predicting the “slot-size” on the radiating patch and inserted “air-gap” between the ground plane and the substrate sheet, simultaneously. Different performance parameters like resonance frequencies, gains, directivities, antenna efficiencies, and radiation efficiencies for dual resonance are observed by varying the dimensions of slot and inserted air-gap. For validation, a prototype of microstrip antenna is fabricated using Roger's substrate, and its performance parameters are measured. Measured results show a very good agreement to their predicted and simulated values.

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