Performance evaluation of Artificial Neural Networks in microstrip fractal antenna parameter estimation

Artificial Neural Networks have been recently used for the design and analysis of fractal antennas. The performance of various types of networks has not been yet explored for these antennas. This paper evaluates the performance of three types of neural networks: Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), and Generalized Regression Neural Networks (GRNN) for parameter estimation of Microstrip Fractal Antenna. Depending on the values of mean percentage error and time taken for training of each type, it has been concluded that the GRNN has best performance among these three networks.