Neural-genetic optimization applied to the design of UWB planar antennas

A novel concept of neural-genetic optimization is presented as applied to ultrawideband planar antennas of ldquobow-tierdquo type with respect to specified values of antenna performance (return loss and gain) in the frequency range 3.1-10.6 GHz. The goal of the optimization is achieved by properly modifying the radiating contour profile of the conventional triangular taper of the bow-tie antenna. The key feature of the optimization is an inversion of artificial neural networks (ANN) with a modular architecture using genetic algorithm (GA). An optimized prototype antenna shows a good impedance matching (return loss < -10 dB) and gain performance (increase from 2 to 5 dB) over the whole frequency range.