This paper discusses bandwidth enhancement for multiband microstrip patch antennas (MMPAs) using symmetrical rectangular/\nsquare slots etched on the patch and the substrate properties. The slot parameters on MMPA are modeled using soft computing\ntechnique of artificial neural networks (ANN). To achieve the best ANN performance, Particle Swarm Optimization (PSO) and\nDifferential Evolution (DE) are applied with ANN�s conventional training algorithm in optimization of the modeling performance.\nIn this study, the slot parameters are assumed as slot distance to the radiating patch edge, slot width, and length. Bandwidth\nenhancement is applied to a formerly designed MMPA fed by a microstrip transmission line attached to the center pin of 50 ohm\nSMA connecter. The simulated antennas are fabricated and measured. Measurement results are utilized for training the artificial\nintelligence models. The ANN provides 98% model accuracy for rectangular slots and 97% for square slots; however, ANFIS offer\n90% accuracy with lack of resonance frequency tracking.
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