The synthesis and analysis of a multiresonant microstrip patch antenna using soft computing techniques are presented. The multiresonance is obtained via attaching inverted L-shaped stubs to the radiated edges of the single frequency patch antenna. The physical geometry of the proposed antenna is synthesized using adaptive-neuro-fuzzy inference systems and the calculated dimensions are applied to the artificial neural network for the analysis process. The return loss and phase of the scattering parameters are computed. The modeled antenna provides 95% accuracy and sufficient results compared with the simulation and measurement results.
This paper discusses bandwidth enhancement for multiband microstrip patch antennas (MMPAs) using symmetrical rectangular/square slots etched on the patch and the substrate properties. The slot parameters on MMPA are modeled using soft computing technique of artificial neural networks (ANN). To achieve the best ANN performance, Particle Swarm Optimization (PSO) and Differential Evolution (DE) are applied with ANN's conventional training algorithm in optimization of the modeling performance. In this study, the slot parameters are assumed as slot distance to the radiating patch edge, slot width, and length. Bandwidth enhancement is applied to a formerly designed MMPA fed by a microstrip transmission line attached to the center pin of 50 ohm SMA connecter. The simulated antennas are fabricated and measured. Measurement results are utilized for training the artificial intelligence models. The ANN provides 98% model accuracy for rectangular slots and 97% for square slots; however, ANFIS offer 90% accuracy with lack of resonance frequency tracking.
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