Levenberg-Marquardt and Conjugate Gradient Neuro-Modeling of Simulated Miniature Rectangular Microstrip Antenna

Neural-network computational modules have recently gained recognition as an unconventional and useful tool for RF and microwave modeling and design. Neural networks can be trained to learn the behaviour of passive/active components/circuits. This work presents artificial neural network (ANN) for design of IE3D simulated miniature microstrip antenna. In the presented work, the artificial neural network is used for accurate determination of different parameters like resonant frequency, bandwidth, return loss, and voltage standing wave ratio (VSWR) of square and rectangular microstrip patch antenna. The developed neural network model which uses the data of simulated hundred antennas is based on Levenberg-Marquardt (LM) and conjugate gradient (CG) feed-back propagation. The developed ANN models for rectangular microstrip antennas (RMSAs) are in very good agreement with the experimental results available in the literature. The comparative analysis of developed models is presented which gives higher accuracy than that reported elsewhere.