Genetic algorithm based inversion of neural networks applied to optimised design of UWB planar antennas

An inversion of artificial neural networks using a genetic algorithm is presented for a novel concept of optimisation applied to UWB planar antennas of bow-tie type with respect to specified values of antenna performance in the frequency range 3.1-10.6 GHz. This efficient concept is shown to achieve significant reduction in computing time for optimisation. The multidimensional inversion is characterised by a simple composite fitness or target function that includes antenna parameters as a function of signal frequency or/and angle dependence. Good impedance matching and gain performance is achieved over the whole frequency range by adequately modifying the radiating contour profile of the conventional triangular bow-tie antenna.