Design of slot loaded proximity coupled microstrip antennas using knowledge based neural networks

Abstract

In this paper, a knowledge based hybrid neural network (KBHNN) is utilized for designing of different slotted proximity coupled microstrip antennas. The slot loaded antennas can be designed from 1 to 6 GHz frequency ranges. By using this model, accuracy is found to be really beneficial, even if the required number of training data has been brought down to half. This method requires less time and scales down the complexities of the design processes. The solutions obtained by this neural approach are compared with the CST simulation results. The results of the KBHNN method are in good accord with the simulated values.