Neurocomputational Analysis of Coaxial Fed Stacked Patch Antennas for Satellite and Wlan Applications

This paper presents a neural network based technique for the analysis of various stacked patch antennas, those can be applied for satellite and wireless local area network (WLAN) applications. In order to show the diversity of artificial neural network (ANN) modeling technique, two different trained neural networks were developed with different number of antenna geometrical parameters as inputs. These trained networks locate the operational resonance frequencies with their bands for stacked patch antennas (SPA) operating in the XKu (8 GHz–18GHz) bands and WLAN bands (2 GHz–6GHz). These frequency bands are useful for satellite communication and indoor wireless communication applications respectively. First ANN model takes design (geometrical) parameters of antenna like lower patch dimension, upper patch dimension, and height of air gap, as a input, whereas other NN model includes feed point location also as a input. The validity of the network is tested with the simulations results obtained from the full-wave Method of Moment (MoM) based IE3D and few experimental results obtained in the laboratory.