Neural Network Technique Simultaneously Optimizing Antenna Return Loss and Radiation Pattern

This paper outlines a new algorithm of neural optimization for viable CAD of complex antenna structures. The input impedance (return loss) and radiation pattern are optimized with respect to the same design variables by a decomposed RBF neural network capable of dealing with various antennas accessible for 3D FDTD analysis. The algorithm features the dynamic generation of as much FDTD data as the network needs to find a solution satisfying the constraints. The performance of the optimization technique is illustrated by its application to a lens antenna constructed as an open-end circular waveguide inserted into a dielectric sphere.