Bandwidth enhancement of patch antennas using neural network dependent modified optimizer

Since a conventional microstrip patch antenna is inherently a narrowband radiator, stacked-patch antennas are commonly used either to enhance the bandwidth or to achieve multi-band characteristics. However, the stacked patch structure has a number of geometrical variables which need to be optimized to achieve the desired characteristics. The conventional design procedure involves repeated costly and time-consuming simulations on an electromagnetic simulator to optimize the various geometrical parameters to arrive at the desired radiation characteristics. In this paper, the task of stacked patch antenna design has been approached as an optimization problem. In order to make a faster CAD module for the stacked-antenna design problem, the simulator has been replaced by a trained artificial neural network (ANN) and embedded in a particle swarm optimization algorithm (PSOA). The ANN is helpful in constructing the “function mapping black-box”, which can relate the frequencies and associated bandwidths of the antenna with its dimensional parameters. The role of the PSOA is to decide the geometrical parameters of the antenna, in response to the designer-specified frequencies and bandwidths. In order to validate the authenticity of the proposed method, a number of antennas have been designed, fabricated, and tested in the laboratory. Simulated and measured results have been compared which establish the accuracy of the proposed technique.